Activity of huggingface/transformers repository

Stable ✅

Constant contribution activity

Why huggingface/transformers is stable?

The result is based on ratio of number of commits from initial and final time ranges.

Initial time range – from 5 Jul, 2023 to 5 Oct, 2023

Final time range – from 5 Apr, 2024 to 5 Jul, 2024

Additions and deletions stats are not available for this repository due to GitHub API limitations.

Data calculated on 5 Jul, 2024

Summary of huggingface/transformers

The Hugging Face's transformers is a state-of-the-art general-purpose library for Natural Language Processing (NLP). It provides thousands of pretrained models to perform tasks on a wide range of NLP tasks, including but not limited, to text classification, information extraction, question answering, summarization, translation, and text generation.

Key features include:

Highly modular and easy integration, where you can choose from high-level pipeline APIs for quick and easy generation/processing or low-level APIs for detailed and custom control.
Strong focus on interoperability, as it works seamlessly with both PyTorch and TensorFlow.
Extensively documented with examples and tutorials, offering guidance on how to use and customize the models.
Large model repository, supporting all major transformer architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc).

These models are suitable for anyone interested in processing large volumes of text data, including researchers, data scientists, or developers. Despite being very powerful, they are easy to use and highly flexible to cater to your specific needs.

- **Project Link:** [transformers](https://github.com/huggingface/transformers)
- **Documentation:** [here](https://huggingface.co/transformers/)
- **Installation:** Install the library via pip (`pip install transformers`).

To use the transformers library, you need a basic understanding of the Python programming language and concepts in machine learning and NLP. You also need a good understanding of the transformer neural model architectures.

Please remember that some models are very computationally heavy, so appropriate compute resources are necessary for training new models or fine-tuning existing ones.