🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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Updated
Jun 10, 2024 - Python
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Neural Network Compression Framework for enhanced OpenVINO™ inference
🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
Generalist and Lightweight Model for Relation Extraction (Extract any relationship types from text)
Toolkit for a learning health system
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Transformers 3rd Edition
Tokenize and convert sample text data into vectors using BERT. Load the vector representation of the text to OpenSearch and use kNN for semantic search
OpenSearch Neural Search example. Load BERT to OpenSearch and create embeddings as data is indexed. Use the embedding to preform vector search
Text-based modeling of materials.
Easy and lightning fast training of 🤗 Transformers on Habana Gaudi processor (HPU)
State of the Art Natural Language Processing
Minimal keyword extraction with BERT
Train transformer-based models.
Implement fine-tuned version of the powerful Llama3 8B model, specifically designed to answer medical questions in an informative way using hugging face pipeline
"Malicious PDF detection" using BERT, which stands for Bidirectional Encoder Representations from Transformers, is a pre-trained deep learning model developed by Google in 2018.
👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
Exploring the Dual Role of AI: RNNs and BERT for Fake News Detection and LLMs for Fake News Generation
What can I do with a LLM model?
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
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