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Github repo with tutorials to fine tune transformers for diff NLP tasks
| Date | Stars |
|---|---|
| 2026-07-31 | 863 |
| 2026-08-02 | 863 |
| 2026-08-06 | 863 |
| 2026-08-18 | 862 |
| 2026-09-13 | 861 |
| 2026-09-20 | 861 |
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# <h1 align="center">PyTorch Transformers Tutorials </h1> <p align="center"> <img alt="Transformer Tutorials" src="meta/banner.png"> </p> <p align="center"> <a href="https://github.com/abhimishra91/transformers-tutorials/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/abhimishra91/transformers-tutorials"></a> <a href="https://github.com/abhimishra91/transformers-tutorials/network"><img alt="GitHub forks" src="https://img.shields.io/github/forks/abhimishra91/transformers-tutorials"></a> <a href="https://github.com/abhimishra91/transformers-tutorials/stargazers"><img alt="Github Stars" src="https://img.shields.io/github/stars/abhimishra91/transformers-tutorials"></a> <a href="https://github.com/abhimishra91/transformers-tutorials/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/abhimishra91/transformers-tutorials"></a> ### Introduction The field of **NLP** was revolutionized in the year 2018 by introduction of **BERT** and his **Transformer** friends(RoBerta, XLM etc.). These novel transformer based neural network architectures and new ways to training a neural network on natural language data introduced transfer learning to NLP problems. Transfer learning had been giving out state of the art results in the Computer Vision domain for a few years now and introduction of transformer models for NLP brought about the same paradigm change in NLP. Companies like [Google](https://github.com/google-research/bert) and [Facebook](https://github.com/pytorch/fairseq/tree/master/examples/roberta) trained their neural networks on large swathes of Natural Language Data to grasp the intricacies of language thereby generating a Language model. Finally these models were fine tuned to specific domain dataset to achieve state of the art results for a specific problem statement. They also published these trained models to open source community. The community members were now able to fine tune these models to their specific use cases. [Hugging Face](https://github.com/huggingface) made it easier for community to access and fine tune these models using their Python Package: [Transformers](https://github.com/huggingface/transformers). ### Motivation Despite these amazing technological advancements applying these solutions to business problems is still a challenge given the niche knowledge required to understand and apply these method on specific problem statements. Hence, In the following tutorials i will be demonstrating how a user can leverage technologies along with some other python tools to fine tune these Language models to specific type of tasks. Before i proceed i will like to mention the following groups for the fantastic work they are doing and sharing which have made these notebooks and tutorials possible: Please review these amazing sources of information and subscribe to their channels/sources. - [Hugging Face Team](https://huggingface.co/) - Abhishek Thakur for his amazing [Youtube videos](https://www.youtube.com/user/abhisheksvnit) The problem statements that i will be working with are: | Notebook |Github Link |Colab Link|Kaggle Kernel| |--|--|--|--| |Text Classification: Multi-Class| [Github](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb) |[](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb)|[Kaggle](https://www.kaggle.com/eggwhites2705/transformers-multiclass-classification-ipynb)| |Text Classification: Multi-Label| [Github](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|[](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_class
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2447f4cff6966e63, topic:nlp, topic:natural-language-processing, topic:named-entity-recognition
matched fp:2447f4cff6966e63, topic:deep-learning, topic:pytorch