Blenderbot Small

Blenderbot Small

Note that BlenderbotSmallModel and BlenderbotSmallForConditionalGeneration are only used in combination with the checkpoint facebook/blenderbot-90M. Larger Blenderbot checkpoints should instead be used with BlenderbotModel and BlenderbotForConditionalGeneration

Overview

The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.

The abstract of the paper is the following:

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

Tips:

Blenderbot Small is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.

This model was contributed by patrickvonplaten. The authors’ code can be found here.

Documentation resources

BlenderbotSmallConfig

class transformers.BlenderbotSmallConfig

( vocab_size = 50265max_position_embeddings = 512encoder_layers = 8encoder_ffn_dim = 2048encoder_attention_heads = 16decoder_layers = 8decoder_ffn_dim = 2048decoder_attention_heads = 16encoder_layerdrop = 0.0decoder_layerdrop = 0.0use_cache = Trueis_encoder_decoder = Trueactivation_function = 'gelu'd_model = 512dropout = 0.1attention_dropout = 0.0activation_dropout = 0.0init_std = 0.02decoder_start_token_id = 1scale_embedding = Falsepad_token_id = 0bos_token_id = 1eos_token_id = 2forced_eos_token_id = 2**kwargs )

Parameters

vocab_size (int, optional, defaults to 50265) — Vocabulary size of the BlenderbotSmall model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling BlenderbotSmallModel or TFBlenderbotSmallModel.
d_model (int, optional, defaults to 512) — Dimensionality of the layers and the pooler layer.
encoder_layers (int, optional, defaults to 8) — Number of encoder layers.
decoder_layers (int, optional, defaults to 8) — Number of decoder layers.
encoder_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (int, optional, defaults to 2048) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
encoder_ffn_dim (int, optional, defaults to 2048) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
activation_function (str or function, 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.
dropout (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
activation_dropout (float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
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).
init_std (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
decoder_layerdrop (float, optional, defaults to 0.0) — The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details.
scale_embedding (bool, optional, defaults to False) — Scale embeddings by diving by sqrt(d_model).
use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (int, optional, defaults to 2) — The id of the token to force as the last generated token when max_length is reached. Usually set to eos_token_id.

This is the configuration class to store the configuration of a BlenderbotSmallModel. It is used to instantiate an BlenderbotSmall 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 BlenderbotSmall facebook/blenderbot_small-90M architecture.

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

Example:

Copied

>>> from transformers import BlenderbotSmallConfig, BlenderbotSmallModel

>>> # Initializing a BlenderbotSmall facebook/blenderbot_small-90M style configuration
>>> configuration = BlenderbotSmallConfig()

>>> # Initializing a model (with random weights) from the facebook/blenderbot_small-90M style configuration
>>> model = BlenderbotSmallModel(configuration)

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

BlenderbotSmallTokenizer

class transformers.BlenderbotSmallTokenizer

( vocab_filemerges_filebos_token = '__start__'eos_token = '__end__'unk_token = '__unk__'pad_token = '__null__'**kwargs )

Parameters

vocab_file (str) — File containing the vocabulary.
merges_file (str) — Path to the merges file.
bos_token (str, optional, defaults to "__start__") — The beginning of sentence token.
eos_token (str, optional, defaults to "__end__") — The end of sentence token.
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. **kwargs — Additional keyword arguments passed along to PreTrainedTokenizer

Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding)

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

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]) — The first tokenized sequence.
token_ids_1 (List[int], optional) — The second tokenized sequence.

Returns

List[int]

The model input with special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens.

This implementation does not add special tokens and this method should be overridden in a subclass.

get_special_tokens_mask

( token_ids_0: typing.Listtoken_ids_1: typing.Optional[typing.List] = Nonealready_has_special_tokens: bool = False ) → A list of integers in the range [0, 1]

Parameters

token_ids_0 (List[int]) — List of ids of the first sequence.
token_ids_1 (List[int], optional) — List of ids of the second sequence.
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

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

Retrieves 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 or encode_plus methods.

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]) — The first tokenized sequence.
token_ids_1 (List[int], optional) — The second tokenized sequence.

Returns

List[int]

The token type ids.

Create the token type IDs corresponding to the sequences passed. What are token type IDs?

Should be overridden in a subclass if the model has a special way of building those.

save_vocabulary

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

BlenderbotSmallTokenizerFast

class transformers.BlenderbotSmallTokenizerFast

( vocab_file = Nonemerges_file = Noneunk_token = '<|endoftext|>'bos_token = '<|endoftext|>'eos_token = '<|endoftext|>'add_prefix_space = Falsetrim_offsets = True**kwargs )

Parameters

vocab_file (str) — Path to the vocabulary file.

Construct a “fast” BlenderbotSmall tokenizer (backed by BOINC AI’s tokenizers library).

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. BlenderbotSmall does not make use of token type ids, therefore a list of zeros is returned.

BlenderbotSmallModel

class transformers.BlenderbotSmallModel

( config: BlenderbotSmallConfig )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.

The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel. 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 torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonedecoder_input_ids: typing.Optional[torch.LongTensor] = Nonedecoder_attention_mask: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Nonedecoder_head_mask: typing.Optional[torch.Tensor] = Nonecross_attn_head_mask: typing.Optional[torch.Tensor] = Noneencoder_outputs: typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = Nonepast_key_values: typing.Optional[typing.List[torch.FloatTensor]] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonedecoder_inputs_embeds: typing.Optional[torch.FloatTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

Parameters

input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.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,
- 0 for tokens that are masked.
What are attention masks?
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
BlenderbotSmall uses the bos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential 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). 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.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
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).
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqModelOutput 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 (BlenderbotSmallConfig) and inputs.

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 decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
decoder_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 of the decoder, 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).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The BlenderbotSmallModel 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, BlenderbotSmallModel

>>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt")  # Batch size 1
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 3, 512]

BlenderbotSmallForConditionalGeneration

class transformers.BlenderbotSmallForConditionalGeneration

( config: BlenderbotSmallConfig )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.

The BlenderbotSmall Model with a language modeling head. Can be used for summarization. This model inherits from PreTrainedModel. 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 torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Nonedecoder_input_ids: typing.Optional[torch.LongTensor] = Nonedecoder_attention_mask: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Nonedecoder_head_mask: typing.Optional[torch.Tensor] = Nonecross_attn_head_mask: typing.Optional[torch.Tensor] = Noneencoder_outputs: typing.Union[typing.Tuple, transformers.modeling_outputs.BaseModelOutput, NoneType] = Nonepast_key_values: typing.Optional[typing.List[torch.FloatTensor]] = Noneinputs_embeds: typing.Optional[torch.Tensor] = Nonedecoder_inputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.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,
- 0 for tokens that are masked.
What are attention masks?
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
BlenderbotSmall uses the bos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential 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). 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.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
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).
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (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

transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput 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 (BlenderbotSmallConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling 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).
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 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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 of the decoder, 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).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The BlenderbotSmallForConditionalGeneration 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.

Conversation example:

Copied

>>> from transformers import AutoTokenizer, BlenderbotSmallForConditionalGeneration

>>> mname = "facebook/blenderbot_small-90M"
>>> model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
Human:  My friends are cool but they eat too many carbs.

>>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
Bot:  what kind of carbs do they eat? i don't know much about carbs.

>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
Human: I'm not sure

>>> NEXT_UTTERANCE = (
...     "My friends are cool but they eat too many carbs.__end__ __start__what kind of carbs do they eat? "
...     "i don't know much about carbs__end__ "
...     "__start__ I'm not sure."
... )
>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
Bot:  they eat a lot of carbs. carbs are high in fat, protein, and fats.

BlenderbotSmallForCausalLM

class transformers.BlenderbotSmallForCausalLM

( config )

forward

( input_ids: LongTensor = Noneattention_mask: typing.Optional[torch.Tensor] = Noneencoder_hidden_states: typing.Optional[torch.FloatTensor] = Noneencoder_attention_mask: typing.Optional[torch.FloatTensor] = Nonehead_mask: typing.Optional[torch.Tensor] = Nonecross_attn_head_mask: typing.Optional[torch.Tensor] = Nonepast_key_values: typing.Optional[typing.List[torch.FloatTensor]] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Parameters

input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.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,
- 0 for tokens that are masked.
What are attention masks?
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]:
head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), 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.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
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 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head). The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential 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).
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (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].
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).
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 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 (BlenderbotSmallConfig) and inputs.

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.

Example:

Copied

>>> from transformers import AutoTokenizer, BlenderbotSmallForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> model = BlenderbotSmallForCausalLM.from_pretrained(
...     "facebook/blenderbot_small-90M", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True

TFBlenderbotSmallModel

class transformers.TFBlenderbotSmallModel

( *args**kwargs )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.

The bare BLENDERBOT_SMALL Model outputting raw hidden-states without any specific head on top. This model inherits from TFPreTrainedModel. 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 tf.keras.Model 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.

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})

Note that when creating models and layers with subclassing 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!

call

( input_ids: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonedecoder_input_ids: tf.Tensor | None = Nonedecoder_attention_mask: tf.Tensor | None = Nonedecoder_position_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Nonedecoder_head_mask: tf.Tensor | None = Nonecross_attn_head_mask: tf.Tensor | None = Noneencoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = Nonepast_key_values: List[tf.Tensor] | None = Noneinputs_embeds: tf.Tensor | None = Nonedecoder_inputs_embeds: tf.Tensor | None = Noneuse_cache: Optional[bool] = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: Optional[bool] = False**kwargs ) → transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)

Parameters

input_ids (tf.Tensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (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,
- 0 for tokens that are masked.
What are attention masks?
decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
BlenderbotSmall uses the bos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
decoder_attention_mask (tf.Tensor of shape (batch_size, target_sequence_length), optional) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
decoder_position_ids (tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tf.FloatTensor, optional) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
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
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
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).

Returns

transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput 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 (BlenderbotSmallConfig) and inputs.

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 decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
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) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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, 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.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFBlenderbotSmallModel 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, TFBlenderbotSmallModel
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> model = TFBlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")

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

>>> last_hidden_states = outputs.last_hidden_state

TFBlenderbotSmallForConditionalGeneration

class transformers.TFBlenderbotSmallForConditionalGeneration

( *args**kwargs )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.

The BLENDERBOT_SMALL Model with a language modeling head. Can be used for summarization. This model inherits from TFPreTrainedModel. 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 tf.keras.Model 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.

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})

Note that when creating models and layers with subclassing 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!

call

( input_ids: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonedecoder_input_ids: tf.Tensor | None = Nonedecoder_attention_mask: tf.Tensor | None = Nonedecoder_position_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Nonedecoder_head_mask: tf.Tensor | None = Nonecross_attn_head_mask: tf.Tensor | None = Noneencoder_outputs: Optional[TFBaseModelOutput] = Nonepast_key_values: List[tf.Tensor] | None = Noneinputs_embeds: tf.Tensor | None = Nonedecoder_inputs_embeds: tf.Tensor | None = Noneuse_cache: Optional[bool] = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonelabels: tf.Tensor | None = Nonetraining: Optional[bool] = False ) → transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)

Parameters

input_ids (tf.Tensor of shape ({0})) — Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (tf.Tensor of shape ({0}), 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.
What are attention masks?
decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
BlenderbotSmall uses the bos_token_id as the starting token for decoder_input_ids generation. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
decoder_attention_mask (tf.Tensor of shape (batch_size, target_sequence_length), optional) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
decoder_position_ids (tf.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tf.FloatTensor, optional) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
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
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
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 either be in [0, ..., config.vocab_size] or -100 (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

transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput 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 (BlenderbotSmallConfig) and inputs.

loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling 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).
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) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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, 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.
encoder_last_hidden_state (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 of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFBlenderbotSmallForConditionalGeneration 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.

Conversation example::

Copied

>>> from transformers import AutoTokenizer, TFBlenderbotSmallForConditionalGeneration

>>> mname = "facebook/blenderbot_small-90M"
>>> model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)

>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
>>> inputs = tokenizer([UTTERANCE], return_tensors="tf")

>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
what kind of carbs do they eat? i don't know much about carbs.

>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
>>> NEXT_UTTERANCE = (
...     "My friends are cool but they eat too many carbs.</s> "
...     "<s>what kind of carbs do they eat? i don't know much about carbs.</s> "
...     "<s>I'm not sure."
... )

>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="tf")
>>> inputs.pop("token_type_ids")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])

FlaxBlenderbotSmallModel

class transformers.FlaxBlenderbotSmallModel

( config: BlenderbotSmallConfiginput_shape: typing.Tuple[int] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = True**kwargs )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) — The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().

The bare BlenderbotSmall Model transformer outputting raw hidden-states without any specific head on top. This model inherits from FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

__call__

( input_ids: Arrayattention_mask: typing.Optional[jax.Array] = Nonedecoder_input_ids: typing.Optional[jax.Array] = Nonedecoder_attention_mask: typing.Optional[jax.Array] = Noneposition_ids: typing.Optional[jax.Array] = Nonedecoder_position_ids: typing.Optional[jax.Array] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)

Returns

transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput 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 (BlenderbotSmallConfig) and inputs.

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 decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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 of the decoder, 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).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallModel

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> model = FlaxBlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")

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

>>> last_hidden_states = outputs.last_hidden_state

encode

( input_ids: Arrayattention_mask: typing.Optional[jax.Array] = Noneposition_ids: typing.Optional[jax.Array] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

Parameters

input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (jnp.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.
What are attention masks?
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].
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutput 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 (<class 'transformers.models.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'>) and inputs.

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.
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.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)

decode

( decoder_input_idsencoder_outputsencoder_attention_mask: typing.Optional[jax.Array] = Nonedecoder_attention_mask: typing.Optional[jax.Array] = Nonedecoder_position_ids: typing.Optional[jax.Array] = Nonepast_key_values: dict = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
encoder_outputs (tuple(tuple(jnp.ndarray)) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (jnp.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.
What are attention masks?
decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions 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 (<class 'transformers.models.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'>) and inputs.

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.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) 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.
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 and config.add_cross_attention=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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

Example:

Copied

>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state

FlaxBlenderbotForConditionalGeneration

class transformers.FlaxBlenderbotSmallForConditionalGeneration

( config: BlenderbotSmallConfiginput_shape: typing.Tuple[int] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = True**kwargs )

Parameters

config (BlenderbotSmallConfig) — 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 from_pretrained() method to load the model weights.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) — The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().

The BLENDERBOT_SMALL Model with a language modeling head. Can be used for summarization. This model inherits from FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

__call__

( input_ids: Arrayattention_mask: typing.Optional[jax.Array] = Nonedecoder_input_ids: typing.Optional[jax.Array] = Nonedecoder_attention_mask: typing.Optional[jax.Array] = Noneposition_ids: typing.Optional[jax.Array] = Nonedecoder_position_ids: typing.Optional[jax.Array] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)

Parameters

input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (jnp.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.
What are attention masks?
decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
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].
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput 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 (BlenderbotSmallConfig) and inputs.

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).
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 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 in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
decoder_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 of the decoder, 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).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The FlaxBlenderbotSmallPreTrainedModel 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.

Summarization example:

Copied

>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np")

>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"]).sequences
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

Mask filling example:

Copied

>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> TXT = "My friends are <mask> but they eat too many carbs."

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> input_ids = tokenizer([TXT], return_tensors="np")["input_ids"]
>>> logits = model(input_ids).logits

>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
>>> values, predictions = jax.lax.top_k(probs)

>>> tokenizer.decode(predictions).split()

encode

( input_ids: Arrayattention_mask: typing.Optional[jax.Array] = Noneposition_ids: typing.Optional[jax.Array] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

Parameters

input_ids (jnp.ndarray of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (jnp.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.
What are attention masks?
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].
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutput 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 (<class 'transformers.models.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'>) and inputs.

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.
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.

Example:

Copied

>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)

decode

( decoder_input_idsencoder_outputsencoder_attention_mask: typing.Optional[jax.Array] = Nonedecoder_attention_mask: typing.Optional[jax.Array] = Nonedecoder_position_ids: typing.Optional[jax.Array] = Nonepast_key_values: dict = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonedeterministic: bool = Trueparams: dict = Nonedropout_rng: PRNGKey = None ) → transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) — Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.
encoder_outputs (tuple(tuple(jnp.ndarray)) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (jnp.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.
What are attention masks?
decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions 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 (<class 'transformers.models.blenderbot_small.configuration_blenderbot_small.BlenderbotSmallConfig'>) and inputs.

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.

Example:

Copied

>>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits

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