docs/source/en/community.md
This page regroups resources around 🤗 Transformers developed by the community.
| Resource | Description | Author |
|---|---|---|
| Hugging Face Transformers Glossary Flashcards | 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. | Darigov Research |
| Notebook | Description | Author | |
|---|---|---|---|
| Fine-tune a pre-trained Transformer to generate lyrics | How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model | Aleksey Korshuk | |
| Train T5 on TPU | How to train T5 on SQUAD with Transformers and Nlp | Suraj Patil | |
| Fine-tune T5 for Classification and Multiple Choice | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning | Suraj Patil | |
| Fine-tune DialoGPT on New Datasets and Languages | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots | Nathan Cooper | |
| Long Sequence Modeling with Reformer | How to train on sequences as long as 500,000 tokens with Reformer | Patrick von Platen | |
| Fine-tune BART for Summarization | How to fine-tune BART for summarization with fastai using blurr | Wayde Gilliam | |
| Fine-tune a pre-trained Transformer on anyone's tweets | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model | Boris Dayma | |
| Optimize 🤗 Hugging Face models with Weights & Biases | A complete tutorial showcasing W&B integration with Hugging Face | Boris Dayma | |
| Pretrain Longformer | How to build a "long" version of existing pretrained models | Iz Beltagy | |
| Fine-tune Longformer for QA | How to fine-tune longformer model for QA task | Suraj Patil | |
| Evaluate Model with 🤗nlp | How to evaluate longformer on TriviaQA with nlp | Patrick von Platen | |
| Fine-tune T5 for Sentiment Span Extraction | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning | Lorenzo Ampil | |
| Fine-tune DistilBert for Multiclass Classification | How to fine-tune DistilBert for multiclass classification with PyTorch | Abhishek Kumar Mishra | |
| Fine-tune BERT for Multi-label Classification | How to fine-tune BERT for multi-label classification using PyTorch | Abhishek Kumar Mishra | |
| Fine-tune T5 for Summarization | How to fine-tune T5 for summarization in PyTorch and track experiments with WandB | Abhishek Kumar Mishra | |
| Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing | How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing | Michael Benesty | |
| Pretrain Reformer for Masked Language Modeling | How to train a Reformer model with bi-directional self-attention layers | Patrick von Platen | |
| Expand and Fine Tune Sci-BERT | How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | Tanmay Thakur | |
| Fine Tune BlenderBotSmall for Summarization using the Trainer API | How to fine-tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | Tanmay Thakur | |
| Fine-tune Electra and interpret with Integrated Gradients | How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | Eliza Szczechla | |
| fine-tune a non-English GPT-2 Model with Trainer class | How to fine-tune a non-English GPT-2 Model with Trainer class | Philipp Schmid | |
| Fine-tune a DistilBERT Model for Multi Label Classification task | How to fine-tune a DistilBERT Model for Multi Label Classification task | Dhaval Taunk | |
| Fine-tune ALBERT for sentence-pair classification | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | Nadir El Manouzi | |
| Fine-tune Roberta for sentiment analysis | How to fine-tune a Roberta model for sentiment analysis | Dhaval Taunk | |
| Evaluating Question Generation Models | How accurate are the answers to questions generated by your seq2seq transformer model? | Pascal Zoleko | |
| Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail | How to warm-start a EncoderDecoderModel with a google-bert/bert-base-uncased checkpoint for summarization on CNN/Dailymail | Patrick von Platen | |
| Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum | How to warm-start a shared EncoderDecoderModel with a FacebookAI/roberta-base checkpoint for summarization on BBC/XSum | Patrick von Platen | |
| Fine-tune TAPAS on Sequential Question Answering (SQA) | How to fine-tune TapasForQuestionAnswering with a tapas-base checkpoint on the Sequential Question Answering (SQA) dataset | Niels Rogge | |
| Evaluate TAPAS on Table Fact Checking (TabFact) | How to evaluate a fine-tuned TapasForSequenceClassification with a tapas-base-finetuned-tabfact checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | Niels Rogge | |
| Fine-tuning mBART for translation | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | Vasudev Gupta | |
| Fine-tune LayoutLM on FUNSD (a form understanding dataset) | How to fine-tune LayoutLMForTokenClassification on the FUNSD dataset for information extraction from scanned documents | Niels Rogge | |
| Fine-Tune DistilGPT2 and Generate Text | How to fine-tune DistilGPT2 and generate text | Aakash Tripathi | |
| Fine-Tune LED on up to 8K tokens | How to fine-tune LED on pubmed for long-range summarization | Patrick von Platen | |
| Evaluate LED on Arxiv | How to effectively evaluate LED on long-range summarization | Patrick von Platen | |
| Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset) | How to fine-tune LayoutLMForSequenceClassification on the RVL-CDIP dataset for scanned document classification | Niels Rogge | |
| Wav2Vec2 CTC decoding with GPT2 adjustment | How to decode CTC sequence with language model adjustment | Eric Lam | |
| Fine-tune BART for summarization in two languages with Trainer class | How to fine-tune BART for summarization in two languages with Trainer class | Eliza Szczechla | |
| Evaluate Big Bird on Trivia QA | How to evaluate BigBird on long document question answering on Trivia QA | Patrick von Platen | |
| Create video captions using Wav2Vec2 | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | Niklas Muennighoff | |
| Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | Niels Rogge | |
| Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | Niels Rogge | |
| Evaluate LUKE on Open Entity, an entity typing dataset | How to evaluate LukeForEntityClassification on the Open Entity dataset | Ikuya Yamada | |
| Evaluate LUKE on TACRED, a relation extraction dataset | How to evaluate LukeForEntityPairClassification on the TACRED dataset | Ikuya Yamada | |
| Evaluate LUKE on CoNLL-2003, an important NER benchmark | How to evaluate LukeForEntitySpanClassification on the CoNLL-2003 dataset | Ikuya Yamada | |
| Evaluate BigBird-Pegasus on PubMed dataset | How to evaluate BigBirdPegasusForConditionalGeneration on PubMed dataset | Vasudev Gupta | |
| Speech Emotion Classification with Wav2Vec2 | How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | Mehrdad Farahani | |
| Detect objects in an image with DETR | How to use a trained DetrForObjectDetection model to detect objects in an image and visualize attention | Niels Rogge | |
| Fine-tune DETR on a custom object detection dataset | How to fine-tune DetrForObjectDetection on a custom object detection dataset | Niels Rogge | |
| Finetune T5 for Named Entity Recognition | How to fine-tune T5 on a Named Entity Recognition Task | Ogundepo Odunayo | |
| Fine-Tuning Open-Source LLM using QLoRA with MLflow and PEFT | How to use QLoRA and PEFT to fine-tune an LLM in a memory-efficient way, while using MLflow to manage experiment tracking | Yuki Watanabe |