Community resources
Community
This page regroups resources around π Transformers developed by the community.
Community resources:
Resource | Description | Author |
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A set of flashcards based on the Transformers Docs Glossary that has been put into a form which can be easily learned/revised using Anki an open source, cross platform app specifically designed for long term knowledge retention. See this Introductory video on how to use the flashcards. |
Community notebooks:
Notebook | Description | Author | |
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How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model | |||
How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | |||
How to train T5 on SQUAD with Transformers and Nlp | |||
How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning | |||
How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots | |||
How to train on sequences as long as 500,000 tokens with Reformer | |||
How to fine-tune BART for summarization with fastai using blurr | |||
How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model | |||
A complete tutorial showcasing W&B integration with BOINC AI | |||
How to build a βlongβ version of existing pretrained models | |||
How to fine-tune longformer model for QA task | |||
How to evaluate longformer on TriviaQA with | |||
How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning | |||
How to fine-tune DistilBert for multiclass classification with PyTorch | |||
How to fine-tune BERT for multi-label classification using PyTorch | |||
How to fine-tune T5 for summarization in PyTorch and track experiments with WandB | |||
How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing | |||
How to train a Reformer model with bi-directional self-attention layers | |||
How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | |||
How to fine-tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | |||
How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | |||
How to fine-tune a non-English GPT-2 Model with Trainer class | |||
How to fine-tune a DistilBERT Model for Multi Label Classification task | |||
How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | |||
How to fine-tune a Roberta model for sentiment analysis | |||
How accurate are the answers to questions generated by your seq2seq transformer model? | |||
How to fine-tune DistilBERT for text classification in TensorFlow | |||
How to warm-start a EncoderDecoderModel with a bert-base-uncased checkpoint for summarization on CNN/Dailymail | |||
How to warm-start a shared EncoderDecoderModel with a roberta-base checkpoint for summarization on BBC/XSum | |||
How to fine-tune TapasForQuestionAnswering with a tapas-base checkpoint on the Sequential Question Answering (SQA) dataset | |||
How to evaluate a fine-tuned TapasForSequenceClassification with a tapas-base-finetuned-tabfact checkpoint using a combination of the π datasets and π transformers libraries | |||
How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | |||
How to fine-tune LayoutLMForTokenClassification on the FUNSD dataset for information extraction from scanned documents | |||
How to fine-tune DistilGPT2 and generate text | |||
How to fine-tune LED on pubmed for long-range summarization | |||
How to effectively evaluate LED on long-range summarization | |||
How to fine-tune LayoutLMForSequenceClassification on the RVL-CDIP dataset for scanned document classification | |||
How to decode CTC sequence with language model adjustment | |||
How to fine-tune BART for summarization in two languages with Trainer class | |||
How to evaluate BigBird on long document question answering on Trivia QA | |||
How to create YouTube captions from any video by transcribing the audio with Wav2Vec | |||
How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using BOINC AI Transformers, Datasets and PyTorch Lightning | |||
How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using BOINC AI Transformers, Datasets and the π Trainer | |||
How to evaluate LukeForEntityClassification on the Open Entity dataset | |||
How to evaluate LukeForEntityPairClassification on the TACRED dataset | |||
How to evaluate LukeForEntitySpanClassification on the CoNLL-2003 dataset | |||
How to evaluate BigBirdPegasusForConditionalGeneration on PubMed dataset | |||
How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | |||
How to use a trained DetrForObjectDetection model to detect objects in an image and visualize attention | |||
How to fine-tune DetrForObjectDetection on a custom object detection dataset | |||
How to fine-tune T5 on a Named Entity Recognition Task |
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