Leveraging BERT and c-TF-IDF to create easily interpretable topics.
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Updated
May 12, 2024 - Python
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
This repository contains demos I made with the Transformers library by HuggingFace.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
Annotations of the interesting ML papers I read
Atividades da disciplina IA024 - Redes Neurais Profundas para Processamento de Linguagem Natural, FEEC-Unicamp, 1s2024
👑 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.
意图槽位模型训练框架(单意图、独立槽位),使用的是JointBERT的结构进行编写,槽位提取时可以选择双指针(Binary Pointer)实体抽取和Global Pointer两种方法。
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai.
A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
A Unified Library for Parameter-Efficient and Modular Transfer Learning
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, learderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表,主要面向大型语言模型评测(例如ChatGPT、LLaMA、GLM、Baichuan等).
快速上手Ai理论及应用实战:基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集,力求每一位能看懂并复现。
LLM projects
Easy and lightning fast training of 🤗 Transformers on Habana Gaudi processor (HPU)
Various LMs/LLMs below 3B parameters (for now) trained using SFT (Supervised Fine Tuning) for several downstream tasks
🔍 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.
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