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On this page
  • XLM-RoBERTa
  • Overview
  • Resources
  • XLMRobertaConfig
  • XLMRobertaTokenizer
  • XLMRobertaTokenizerFast
  • XLMRobertaModel
  • XLMRobertaForCausalLM
  • XLMRobertaForMaskedLM
  • XLMRobertaForSequenceClassification
  • XLMRobertaForMultipleChoice
  • XLMRobertaForTokenClassification
  • XLMRobertaForQuestionAnswering
  • TFXLMRobertaModel
  • TFXLMRobertaForCausalLM
  • TFXLMRobertaForMaskedLM
  • TFXLMRobertaForSequenceClassification
  • TFXLMRobertaForMultipleChoice
  • TFXLMRobertaForTokenClassification
  • TFXLMRobertaForQuestionAnswering
  • FlaxXLMRobertaModel
  • FlaxXLMRobertaForCausalLM
  • FlaxXLMRobertaForMaskedLM
  • FlaxXLMRobertaForSequenceClassification
  • FlaxXLMRobertaForMultipleChoice
  • FlaxXLMRobertaForTokenClassification
  • FlaxXLMRobertaForQuestionAnswering
  1. API
  2. MODELS
  3. TEXT MODELS

XLM-RoBERTa

PreviousXLM-ProphetNetNextXLM-RoBERTa-XL

Last updated 1 year ago

XLM-RoBERTa

Overview

The XLM-RoBERTa model was proposed in by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

The abstract from the paper is the following:

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

Tips:

  • XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does not require lang tensors to understand which language is used, and should be able to determine the correct language from the input ids.

  • Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses masked language modeling on sentences coming from one language.

  • This implementation is the same as RoBERTa. Refer to the for usage examples as well as the information relative to the inputs and outputs.

This model was contributed by . The original code can be found .

Resources

A list of official BOINC AI and community (indicated by 🌎) resources to help you get started with XLM-RoBERTa. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Text Classification

  • A blog post on how to

  • is supported by this and .

  • is supported by this and .

  • is supported by this and .

  • chapter of the 🌎 BOINC AI Task Guides.

Token Classification

Text Generation

Fill-Mask

Question Answering

Multiple choice

🚀 Deploy

XLMRobertaConfig

class transformers.XLMRobertaConfig

( vocab_size = 30522hidden_size = 768num_hidden_layers = 12num_attention_heads = 12intermediate_size = 3072hidden_act = 'gelu'hidden_dropout_prob = 0.1attention_probs_dropout_prob = 0.1max_position_embeddings = 512type_vocab_size = 2initializer_range = 0.02layer_norm_eps = 1e-12pad_token_id = 1bos_token_id = 0eos_token_id = 2position_embedding_type = 'absolute'use_cache = Trueclassifier_dropout = None**kwargs )

Parameters

  • hidden_size (int, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.

  • intermediate_size (int, optional, defaults to 3072) — Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.

  • hidden_act (str or Callable, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.

  • hidden_dropout_prob (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_probs_dropout_prob (float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.

  • max_position_embeddings (int, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • layer_norm_eps (float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.

  • is_decoder (bool, optional, defaults to False) — Whether the model is used as a decoder or not. If False, the model is used as an encoder.

  • use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

  • classifier_dropout (float, optional) — The dropout ratio for the classification head.

Examples:

Copied

>>> from transformers import XLMRobertaConfig, XLMRobertaModel

>>> # Initializing a XLM-RoBERTa xlm-roberta-base style configuration
>>> configuration = XLMRobertaConfig()

>>> # Initializing a model (with random weights) from the xlm-roberta-base style configuration
>>> model = XLMRobertaModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

XLMRobertaTokenizer

class transformers.XLMRobertaTokenizer

( vocab_filebos_token = '<s>'eos_token = '</s>'sep_token = '</s>'cls_token = '<s>'unk_token = '<unk>'pad_token = '<pad>'mask_token = '<mask>'sp_model_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None**kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.

  • bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

    When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.

  • eos_token (str, optional, defaults to "</s>") — The end of sequence token.

    When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.

  • sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

  • cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

  • unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

  • pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.

  • mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

  • additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.

    • enable_sampling: Enable subword regularization.

    • nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.

      • nbest_size = {0,1}: No sampling is performed.

      • nbest_size > 1: samples from the nbest_size results.

      • nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.

    • alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

  • sp_model (SentencePieceProcessor) — The SentencePiece processor that is used for every conversion (string, tokens and IDs).

build_inputs_with_special_tokens

( token_ids_0: typing.List[int]token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs to which the special tokens will be added.

  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format:

  • single sequence: <s> X </s>

  • pair of sequences: <s> A </s></s> B </s>

get_special_tokens_mask

( token_ids_0: typing.List[int]token_ids_1: typing.Optional[typing.List[int]] = Nonealready_has_special_tokens: bool = False ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.

  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

  • already_has_special_tokens (bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model.

Returns

List[int]

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

create_token_type_ids_from_sequences

( token_ids_0: typing.List[int]token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.

  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

List of zeros.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned.

save_vocabulary

( save_directory: strfilename_prefix: typing.Optional[str] = None )

XLMRobertaTokenizerFast

class transformers.XLMRobertaTokenizerFast

( vocab_file = Nonetokenizer_file = Nonebos_token = '<s>'eos_token = '</s>'sep_token = '</s>'cls_token = '<s>'unk_token = '<unk>'pad_token = '<pad>'mask_token = '<mask>'**kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.

  • bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

    When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.

  • eos_token (str, optional, defaults to "</s>") — The end of sequence token.

    When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.

  • sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

  • cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

  • unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

  • pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.

  • mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

  • additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.

build_inputs_with_special_tokens

( token_ids_0: typing.List[int]token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs to which the special tokens will be added.

  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format:

  • single sequence: <s> X </s>

  • pair of sequences: <s> A </s></s> B </s>

create_token_type_ids_from_sequences

( token_ids_0: typing.List[int]token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

  • token_ids_0 (List[int]) — List of IDs.

  • token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

Returns

List[int]

List of zeros.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned.

XLMRobertaModel

class transformers.XLMRobertaModel

( configadd_pooling_layer = True )

Parameters

The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

To behave as an decoder the model needs to be initialized with the is_decoder argument of the configuration set to True. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and add_cross_attention set to True; an encoder_hidden_states is then expected as an input to the forward pass.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

  • encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

    If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

Returns

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = XLMRobertaModel.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state

XLMRobertaForCausalLM

class transformers.XLMRobertaForCausalLM

( config )

Parameters

XLM-RoBERTa Model with a language modeling head on top for CLM fine-tuning.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • encoder_hidden_states (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

  • encoder_attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

  • past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

    If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.

    Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaForCausalLM, AutoConfig
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
>>> config = AutoConfig.from_pretrained("roberta-base")
>>> config.is_decoder = True
>>> model = XLMRobertaForCausalLM.from_pretrained("roberta-base", config=config)

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> prediction_logits = outputs.logits

XLMRobertaForMaskedLM

class transformers.XLMRobertaForMaskedLM

( config )

Parameters

XLM-RoBERTa Model with a language modeling head on top.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

  • kwargs (Dict[str, any], optional, defaults to {}) — Used to hide legacy arguments that have been deprecated.

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaForMaskedLM
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = XLMRobertaForMaskedLM.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # retrieve index of <mask>
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'

>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> # mask labels of non-<mask> tokens
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)
>>> round(outputs.loss.item(), 2)
0.1

XLMRobertaForSequenceClassification

class transformers.XLMRobertaForSequenceClassification

( config )

Parameters

XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.

  • logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example of single-label classification:

Copied

>>> import torch
>>> from transformers import AutoTokenizer, XLMRobertaForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = XLMRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'optimism'

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = XLMRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.08

Example of multi-label classification:

Copied

>>> import torch
>>> from transformers import AutoTokenizer, XLMRobertaForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = XLMRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", problem_type="multi_label_classification")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = XLMRobertaForSequenceClassification.from_pretrained(
...     "cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss

XLMRobertaForMultipleChoice

class transformers.XLMRobertaForMultipleChoice

( config )

Parameters

XLM-RoBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, num_choices, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See input_ids above)

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.

  • logits (torch.FloatTensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).

    Classification scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaForMultipleChoice
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = XLMRobertaForMultipleChoice.from_pretrained("xlm-roberta-base")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logits

XLMRobertaForTokenClassification

class transformers.XLMRobertaForTokenClassification

( config )

Parameters

XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
>>> model = XLMRobertaForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")

>>> inputs = tokenizer(
...     "BOINCAI is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_token_class_ids = logits.argmax(-1)

>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.01

XLMRobertaForQuestionAnswering

class transformers.XLMRobertaForQuestionAnswering

( config )

Parameters

XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.

  • start_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

  • end_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

  • start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).

  • end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, XLMRobertaForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
>>> model = XLMRobertaForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
' puppet'

>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss
>>> round(loss.item(), 2)
0.86

TFXLMRobertaModel

class transformers.TFXLMRobertaModel

( *args**kwargs )

Parameters

The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • encoder_hidden_states (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

  • encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) — contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

  • use_cache (bool, optional, defaults to True) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation

Returns

  • last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

    This output is usually not a good summary of the semantic content of the input, you’re often better with averaging or pooling the sequence of hidden-states for the whole input sequence.

  • past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

    Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, TFXLMRobertaModel
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = TFXLMRobertaModel.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)

>>> last_hidden_states = outputs.last_hidden_state

TFXLMRobertaForCausalLM

class transformers.TFXLMRobertaForCausalLM

( *args**kwargs )

Parameters

XLM-RoBERTa Model with a language modeling head on top for CLM fine-tuning.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • encoder_hidden_states (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

  • encoder_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) — contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

  • use_cache (bool, optional, defaults to True) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation

  • labels (tf.Tensor or np.ndarray of shape (batch_size, sequence_length), optional) — Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1].

Returns

  • loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

    Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, TFXLMRobertaForCausalLM
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = TFXLMRobertaForCausalLM.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logits

TFXLMRobertaForMaskedLM

class transformers.TFXLMRobertaForMaskedLM

( *args**kwargs )

Parameters

XLM RoBERTa Model with a language modeling head on top.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • labels (tf.Tensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

Returns

  • loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Masked language modeling (MLM) loss.

  • logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, TFXLMRobertaForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = TFXLMRobertaForMaskedLM.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="tf")
>>> logits = model(**inputs).logits

>>> # retrieve index of <mask>
>>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
>>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)

>>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'

Copied

>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>> # mask labels of non-<mask> tokens
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)
>>> round(float(outputs.loss), 2)
0.1

TFXLMRobertaForSequenceClassification

class transformers.TFXLMRobertaForSequenceClassification

( *args**kwargs )

Parameters

XLM RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • labels (tf.Tensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

  • loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.

  • logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

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>>> from transformers import AutoTokenizer, TFXLMRobertaForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = TFXLMRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")

>>> logits = model(**inputs).logits

>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'optimism'

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>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = TFXLMRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)

>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(float(loss), 2)
0.08

TFXLMRobertaForMultipleChoice

class transformers.TFXLMRobertaForMultipleChoice

( *args**kwargs )

Parameters

XLM Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • labels (tf.Tensor of shape (batch_size,), optional) — Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices] where num_choices is the size of the second dimension of the input tensors. (See input_ids above)

Returns

  • loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification loss.

  • logits (tf.Tensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).

    Classification scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

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>>> from transformers import AutoTokenizer, TFXLMRobertaForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = TFXLMRobertaForMultipleChoice.from_pretrained("xlm-roberta-base")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> logits = outputs.logits

TFXLMRobertaForTokenClassification

class transformers.TFXLMRobertaForTokenClassification

( *args**kwargs )

Parameters

XLM RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • labels (tf.Tensor of shape (batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

Returns

  • loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided) — Classification loss.

  • logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

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>>> from transformers import AutoTokenizer, TFXLMRobertaForTokenClassification
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("ydshieh/roberta-large-ner-english")
>>> model = TFXLMRobertaForTokenClassification.from_pretrained("ydshieh/roberta-large-ner-english")

>>> inputs = tokenizer(
...     "BOINCAI is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )

>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)

>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']

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>>> labels = predicted_token_class_ids
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
>>> round(float(loss), 2)
0.01

TFXLMRobertaForQuestionAnswering

class transformers.TFXLMRobertaForQuestionAnswering

( *args**kwargs )

Parameters

XLM RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

Parameters

  • attention_mask (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

  • head_mask (Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.

  • training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

  • start_positions (tf.Tensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

  • end_positions (tf.Tensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns

  • loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

  • start_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).

  • end_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

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>>> from transformers import AutoTokenizer, TFXLMRobertaForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("ydshieh/roberta-base-squad2")
>>> model = TFXLMRobertaForQuestionAnswering.from_pretrained("ydshieh/roberta-base-squad2")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)

>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
' puppet'

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>>> # target is "nice puppet"
>>> target_start_index = tf.constant([14])
>>> target_end_index = tf.constant([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = tf.math.reduce_mean(outputs.loss)
>>> round(float(loss), 2)
0.86

FlaxXLMRobertaModel

class transformers.FlaxXLMRobertaModel

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

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>>> from transformers import AutoTokenizer, FlaxXLMRobertaModel

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state

FlaxXLMRobertaForCausalLM

class transformers.FlaxXLMRobertaForCausalLM

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.

    Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForCausalLM.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
>>> outputs = model(**inputs)

>>> # retrieve logts for next token
>>> next_token_logits = outputs.logits[:, -1]

FlaxXLMRobertaForMaskedLM

class transformers.FlaxXLMRobertaForMaskedLM

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM RoBERTa Model with a language modeling head on top.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForMaskedLM

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForMaskedLM.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")

>>> outputs = model(**inputs)
>>> logits = outputs.logits

FlaxXLMRobertaForSequenceClassification

class transformers.FlaxXLMRobertaForSequenceClassification

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • logits (jnp.ndarray of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForSequenceClassification.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

>>> outputs = model(**inputs)
>>> logits = outputs.logits

FlaxXLMRobertaForMultipleChoice

class transformers.FlaxXLMRobertaForMultipleChoice

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • logits (jnp.ndarray of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).

    Classification scores (before SoftMax).

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForMultipleChoice

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForMultipleChoice.from_pretrained("xlm-roberta-base")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
>>> outputs = model(**{k: v[None, :] for k, v in encoding.items()})

>>> logits = outputs.logits

FlaxXLMRobertaForTokenClassification

class transformers.FlaxXLMRobertaForTokenClassification

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForTokenClassification

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForTokenClassification.from_pretrained("xlm-roberta-base")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")

>>> outputs = model(**inputs)
>>> logits = outputs.logits

FlaxXLMRobertaForQuestionAnswering

class transformers.FlaxXLMRobertaForQuestionAnswering

( config: XLMRobertaConfiginput_shape: typing.Tuple = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = Truegradient_checkpointing: bool = False**kwargs )

Parameters

XLM Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).

Finally, this model supports inherent JAX features such as:

__call__

Parameters

  • input_ids (numpy.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • attention_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

  • token_type_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

Returns

  • start_logits (jnp.ndarray of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).

  • end_logits (jnp.ndarray of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).

  • hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxXLMRobertaPreTrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxXLMRobertaForQuestionAnswering

>>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
>>> model = FlaxXLMRobertaForQuestionAnswering.from_pretrained("xlm-roberta-base")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="jax")

>>> outputs = model(**inputs)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits

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chapter of the 🌎 BOINC AI Course.

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A blog post on how to .

vocab_size (int, optional, defaults to 30522) — Vocabulary size of the XLM-RoBERTa model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling or .

type_vocab_size (int, optional, defaults to 2) — The vocabulary size of the token_type_ids passed when calling or .

position_embedding_type (str, optional, defaults to "absolute") — Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For positional embeddings use "absolute". For more information on "relative_key", please refer to . For more information on "relative_key_query", please refer to Method 4 in .

This is the configuration class to store the configuration of a or a . It is used to instantiate a XLM-RoBERTa model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the XLMRoBERTa architecture.

Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.

sp_model_kwargs (dict, optional) — Will be passed to the SentencePieceProcessor.__init__() method. The can be used, among other things, to set:

Adapted from and . Based on .

This tokenizer inherits from which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

List of with the appropriate special tokens.

Construct a “fast” XLM-RoBERTa tokenizer (backed by BOINCAI’s tokenizers library). Adapted from and . Based on .

This tokenizer inherits from which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

List of with the appropriate special tokens.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

.. _Attention is all you need:

( input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonetoken_type_ids: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.Tensor] = Noneencoder_attention_mask: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[typing.List[torch.FloatTensor]] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneencoder_hidden_states: typing.Optional[torch.FloatTensor] = Noneencoder_attention_mask: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Nonepast_key_values: typing.Tuple[typing.Tuple[torch.FloatTensor]] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneencoder_hidden_states: typing.Optional[torch.FloatTensor] = Noneencoder_attention_mask: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonestart_positions: typing.Optional[torch.LongTensor] = Noneend_positions: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneencoder_hidden_states: np.ndarray | tf.Tensor | None = Noneencoder_attention_mask: np.ndarray | tf.Tensor | None = Nonepast_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = Noneuse_cache: Optional[bool] = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneencoder_hidden_states: np.ndarray | tf.Tensor | None = Noneencoder_attention_mask: np.ndarray | tf.Tensor | None = Nonepast_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = Noneuse_cache: Optional[bool] = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note that when creating models and layers with then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

( input_ids: TFModelInputType | None = Noneattention_mask: np.ndarray | tf.Tensor | None = Nonetoken_type_ids: np.ndarray | tf.Tensor | None = Noneposition_ids: np.ndarray | tf.Tensor | None = Nonehead_mask: np.ndarray | tf.Tensor | None = Noneinputs_embeds: np.ndarray | tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonestart_positions: np.ndarray | tf.Tensor | None = Noneend_positions: np.ndarray | tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using . See and for details.

0 for tokens that are masked.

1 corresponds to a sentence B token.

position_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

config () — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the method to load the model weights.

This model inherits from . Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

This model is also a Flax Linen subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

( input_idsattention_mask = Nonetoken_type_ids = Noneposition_ids = Nonehead_mask = Noneencoder_hidden_states = Noneencoder_attention_mask = Noneparams: dict = Nonedropout_rng: PRNGKey = Nonetrain: bool = Falseoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepast_key_values: dict = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

🌍
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🌍
XLMRobertaForTokenClassification
example script
notebook
TFXLMRobertaForTokenClassification
example script
notebook
FlaxXLMRobertaForTokenClassification
example script
Token classification
Token classification task guide
XLMRobertaForCausalLM
example script
notebook
Causal language modeling
Causal language modeling task guide
XLMRobertaForMaskedLM
example script
notebook
TFXLMRobertaForMaskedLM
example script
notebook
FlaxXLMRobertaForMaskedLM
example script
notebook
Masked language modeling
Masked language modeling
XLMRobertaForQuestionAnswering
example script
notebook
TFXLMRobertaForQuestionAnswering
example script
notebook
FlaxXLMRobertaForQuestionAnswering
example script
Question answering
Question answering task guide
XLMRobertaForMultipleChoice
example script
notebook
TFXLMRobertaForMultipleChoice
example script
notebook
Multiple choice task guide
Deploy Serverless XLM RoBERTa on AWS Lambda
<source>
XLMRobertaModel
TFXLMRobertaModel
XLMRobertaModel
TFXLMRobertaModel
Self-Attention with Relative Position Representations (Shaw et al.)
Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
XLMRobertaModel
TFXLMRobertaModel
xlm-roberta-base
PretrainedConfig
PretrainedConfig
<source>
Python wrapper for SentencePiece
RobertaTokenizer
XLNetTokenizer
SentencePiece
PreTrainedTokenizer
<source>
input IDs
<source>
<source>
<source>
<source>
RobertaTokenizer
XLNetTokenizer
BPE
PreTrainedTokenizerFast
<source>
input IDs
<source>
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
https://arxiv.org/abs/1706.03762
<source>
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
XLMRobertaConfig
XLMRobertaModel
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions
XLMRobertaConfig
XLMRobertaForCausalLM
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.MaskedLMOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.MaskedLMOutput
transformers.modeling_outputs.MaskedLMOutput
XLMRobertaConfig
XLMRobertaForMaskedLM
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.SequenceClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.SequenceClassifierOutput
transformers.modeling_outputs.SequenceClassifierOutput
XLMRobertaConfig
XLMRobertaForSequenceClassification
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.MultipleChoiceModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.MultipleChoiceModelOutput
transformers.modeling_outputs.MultipleChoiceModelOutput
XLMRobertaConfig
XLMRobertaForMultipleChoice
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.TokenClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.TokenClassifierOutput
transformers.modeling_outputs.TokenClassifierOutput
XLMRobertaConfig
XLMRobertaForTokenClassification
<source>
XLMRobertaConfig
from_pretrained()
PreTrainedModel
torch.nn.Module
<source>
transformers.modeling_outputs.QuestionAnsweringModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.QuestionAnsweringModelOutput
transformers.modeling_outputs.QuestionAnsweringModelOutput
XLMRobertaConfig
XLMRobertaForQuestionAnswering
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
XLMRobertaConfig
TFXLMRobertaModel
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
XLMRobertaConfig
TFXLMRobertaForCausalLM
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFMaskedLMOutput
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFMaskedLMOutput
transformers.modeling_tf_outputs.TFMaskedLMOutput
XLMRobertaConfig
TFXLMRobertaForMaskedLM
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
XLMRobertaConfig
TFXLMRobertaForSequenceClassification
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput
XLMRobertaConfig
TFXLMRobertaForMultipleChoice
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFTokenClassifierOutput
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFTokenClassifierOutput
transformers.modeling_tf_outputs.TFTokenClassifierOutput
XLMRobertaConfig
TFXLMRobertaForTokenClassification
<source>
XLMRobertaConfig
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
AutoTokenizer
PreTrainedTokenizer.call()
PreTrainedTokenizer.encode()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
XLMRobertaConfig
TFXLMRobertaForQuestionAnswering
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput
transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxTokenClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxTokenClassifierOutput
transformers.modeling_flax_outputs.FlaxTokenClassifierOutput
XLMRobertaConfig
<source>
XLMRobertaConfig
from_pretrained()
FlaxPreTrainedModel
flax.linen.Module
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
<source>
transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
ModelOutput
transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
XLMRobertaConfig
Unsupervised Cross-lingual Representation Learning at Scale
documentation of RoBERTa
stefan-it
here
finetune XLM RoBERTa for multiclass classification with Habana Gaudi on AWS
XLMRobertaForSequenceClassification
example script
notebook
TFXLMRobertaForSequenceClassification
example script
notebook
FlaxXLMRobertaForSequenceClassification
example script
notebook
Text classification
Text classification task guide