🤗 Transformers Notebooks¶
You can find here a list of the official notebooks provided by Hugging Face.
Also, we would like to list here interesting content created by the community. If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a Pull Request so it can be included under the Community notebooks.
Hugging Face’s notebooks 🤗¶
| Notebook | Description | |
|---|---|---|
| Getting Started Tokenizers | How to train and use your very own tokenizer |  |
| Getting Started Transformers | How to easily start using transformers | |
| How to use Pipelines | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | |
| How to fine-tune a model on text classification | Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | |
| How to fine-tune a model on language modeling | Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | |
| How to fine-tune a model on token classification | Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | |
| How to fine-tune a model on question answering | Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | |
| How to train a language model from scratch | Highlight all the steps to effectively train Transformer model on custom data | |
| How to generate text | How to use different decoding methods for language generation with transformers | |
| How to export model to ONNX | Highlight how to export and run inference workloads through ONNX | |
| How to use Benchmarks | How to benchmark models with transformers | |
| Reformer | How Reformer pushes the limits of language modeling | |