{"id":19606526,"url":"https://github.com/ankitamungalpara/huggingface-nlp","last_synced_at":"2026-04-30T11:36:04.569Z","repository":{"id":252151251,"uuid":"839053949","full_name":"AnkitaMungalpara/HuggingFace-NLP","owner":"AnkitaMungalpara","description":"This repository introduces the 🤗 Transformers library, covering Transformer models, fine-tuning on datasets, and result sharing. You’ll learn to handle classic NLP tasks using 🤗 Datasets and 🤗 Tokenizers. 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By the end of this section, you’ll know how to utilize a model from the Hugging Face Hub, fine-tune it on a dataset, and share your results.\n\n- [01. Introduction to Transformers Pipeline](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/00_Transformers_Pipeline_Introduction.ipynb)\n  \n- [02. Transformer Pipelines: Behind the Scenes](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/01_Behind_the_scenes_pipeline.ipynb)\n  \n- [03. Models and Tokenizers](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/02_Transformers_Models_and_Tokenizers.ipynb)\n\n- [04. Handling Multiple Sequences](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/03_Handling_Multiple_Sequences_Transformers.ipynb)\n\n\n### Module 2: Fundamentals of 🤗 Datasets and Tokenizers for NLP\n\nThis module covers the foundational concepts of working with 🤗 Datasets and 🤗 Tokenizers, preparing to independently solve common NLP tasks.\n\n- [01. Processing Large Data](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/04_Processing_Data_Hugging_Face_Transformers.ipynb)\n  \n- [02. Full Training with GPU and Accelerator](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/06_Full_Training_HuggingFace_Transformers.ipynb)\n\n- [03. Datasets in HuggingFace](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/07_Datasets_in_HuggingFace.ipynb)\n\n- [04. Semantic Search with FAISS](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/08_Semantic_Search_with_FAISS.ipynb)\n\n  \u003ctable\u003e\n      \u003cthead\u003e\n          \u003ctr\u003e\n              \u003cth\u003eSection\u003c/th\u003e\n              \u003cth\u003eDescription\u003c/th\u003e\n              \u003cth\u003eLinks\u003c/th\u003e\n          \u003c/tr\u003e\n      \u003c/thead\u003e\n      \u003ctbody\u003e\n          \u003ctr\u003e\n              \u003ctd\u003eUsing embeddings for semantic search\u003c/td\u003e\n              \u003ctd\u003eIntroduction to building a semantic search engine using embeddings.\u003c/td\u003e\n              \u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/index\"\u003eTransformers Documentation\u003c/a\u003e\u003c/td\u003e\n          \u003c/tr\u003e\n          \u003ctr\u003e\n              \u003ctd\u003eLoading and Preparing Dataset\u003c/td\u003e\n              \u003ctd\u003eLoading the GitHub Issues dataset and filtering out pull requests to focus on issues with comments.\u003c/td\u003e\n              \u003ctd\u003e\u003ca href=\"https://huggingface.co/datasets/lewtun/github-issues\"\u003eGitHub Issues Dataset\u003c/a\u003e\u003c/td\u003e\n          \u003c/tr\u003e\n          \u003ctr\u003e\n              \u003ctd\u003eCreating Text Embeddings\u003c/td\u003e\n              \u003ctd\u003eUsing the sentence-transformers library to create embeddings for text data, with a focus on pooling techniques.\u003c/td\u003e\n              \u003ctd\u003e\u003ca href=\"https://www.sbert.net/\"\u003eSentence-Transformers Documentation\u003c/a\u003e\u003c/td\u003e\n          \u003c/tr\u003e\n          \u003ctr\u003e\n              \u003ctd\u003eUsing FAISS for Efficient Similarity Search\u003c/td\u003e\n              \u003ctd\u003eImplementing FAISS to create an index for fast similarity searches on the embeddings and conducting nearest neighbor searches.\u003c/td\u003e\n              \u003ctd\u003e\u003ca href=\"https://faiss.ai/\"\u003eFAISS Documentation\u003c/a\u003e\u003c/td\u003e\n          \u003c/tr\u003e\n      \u003c/tbody\u003e\n  \u003c/table\u003e\n  \n- [05. Training New Tokenizer](https://github.com/AnkitaMungalpara/HuggingFace-NLP/blob/main/09_Training_new_tokenizer.ipynb)\n\n  \u003c!--\n  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AnkitaMungalpara/HuggingFace-NLP/blob/main/08_Semantic_Search_with_FAISS.ipynb)\n\nExplore applications of Transformer models in speech processing and computer vision. This section will prepare you to build and share model demos and optimize them for production environments, enabling you to apply 🤗 Transformers to various machine learning challenges.\n\n--\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankitamungalpara%2Fhuggingface-nlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fankitamungalpara%2Fhuggingface-nlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankitamungalpara%2Fhuggingface-nlp/lists"}