Explore

Building the Embedding with HuggingSpace

HuggingFace Transformers Course:

https://huggingface.co/learn/nlp-course/chapter1/1⁠

⁠

1. Getting Started With Embeddings - Hugging Face: `https://huggingface.co/blog/getting-started-with-embeddings`

2. Question answering - Hugging Face: `https://huggingface.co/docs/transformers/tasks/question_answering`

3. Semantic search with FAISS - Hugging Face NLP Course: `https://huggingface.co/learn/nlp-course/chapter5/6?fw=tf`

4. Document Question Answering - Hugging Face: `https://huggingface.co/docs/transformers/tasks/document_question_answering`

5. Hugging Face's Text Embeddings Inference Library - YouTube: `https://youtube.com/watch?v=tvs350imHLY`

6. An Introduction to Using Transformers and Hugging Face | DataCamp: `https://www.datacamp.com/tutorial/an-introduction-to-using-transformers-and-hugging-face`

[1] https://huggingface.co/blog/getting-started-with-embeddings

[2] https://huggingface.co/docs/transformers/tasks/question_answering

[3] https://huggingface.co/learn/nlp-course/chapter5/6?fw=tf

[4] https://huggingface.co/docs/transformers/tasks/document_question_answering

[5]

https://youtube.com/watch?v=tvs350imHLY⁠

⁠

[6] https://www.datacamp.com/tutorial/an-introduction-to-using-transformers-and-hugging-face

Hugging Face provides several resources to help you build embeddings for your AI model. Here's a step-by-step guide based on the information from the search results:

1. **Understanding Embeddings**: An embedding is a numerical representation of a piece of information, such as text. Hugging Face provides a tutorial on creating a small Frequently Asked Questions (FAQs) engine using embeddings. This involves embedding a dataset using the Hugging Face Inference API, which can be done with a quick POST call[1].

2. **Creating Text Embeddings**: You can create embeddings using the AutoModel class in Hugging Face. This involves loading a suitable checkpoint to load the model from, placing the model and inputs on a GPU device for faster processing, and pooling or averaging the token embeddings to create a vector representation for whole sentences, paragraphs, or documents[2].

3. **Using FAISS for Semantic Search**: Once you have a dataset of embeddings, you can use a FAISS index to search over them. FAISS (short for Facebook AI Similarity Search) is a library that provides efficient algorithms to quickly search and cluster embedding vectors[2].

4. **Deploying Embedding Models**: Hugging Face Inference Endpoints offer an easy and secure way to deploy Machine Learning models for use in production. This includes deploying state-of-the-art embeddings models with industry-leading throughput and cost efficiency[7].

5. **Fine-tuning a Pretrained Model**: Hugging Face provides a Trainer class optimized for training their models. This class supports a wide range of training options and features such as logging, gradient accumulation, and mixed precision. You can fine-tune a pretrained model with this class[8].

6. **Training and Fine-Tuning Sentence Transformers Models**: In a Sentence Transformer model, you map a variable-length text to a fixed-size embedding representing that input's meaning. You can fine-tune an existing Sentence Transformers model or define a new one[5].

Remember to check out the Hugging Face NLP Course[4] and their blog posts[1][5][7][11] for more detailed tutorials and guides. You can also find video tutorials on YouTube[3][6][9][10] that provide visual demonstrations on how to build embeddings with Hugging Face.

Citations: [1] https://huggingface.co/blog/getting-started-with-embeddings [2] https://huggingface.co/learn/nlp-course/chapter5/6?fw=tf [3] https://youtube.com/watch?v=tvs350imHLY&t=0 [4] https://huggingface.co/learn/nlp-course [5] https://huggingface.co/blog/how-to-train-sentence-transformers [6] https://youtube.com/watch?v=RJccSbJ9Go4&t=0 [7] https://huggingface.co/blog/inference-endpoints-embeddings [8] https://huggingface.co/docs/transformers/training [9] https://youtube.com/watch?v=ZUl-u3uKIa4&t=14 [10] https://youtube.com/watch?v=T6GPytSmUQM&t=11 [11] https://huggingface.co/blog/your-first-ml-project [12] https://youtube.com/watch?v=ld-oFsluf8Q [13] https://discuss.huggingface.co/t/training-bert-for-word-embedding/20322 [14] https://huggingface.co/blog/intro-graphml [15] https://youtube.com/watch?v=At8JGjxEAcE [16] https://youtube.com/watch?v=e_NFeXLIwwo [17] https://huggingface.co/blog/vision_language_pretraining [18] https://youtube.com/watch?v=ZB1nn3JWyec

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.