Transformers Libraries

22 Components & Libraries

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Transformers Libraries

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. Thes…

:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

This command installs everything needed for basic Pipelines that use an in-memory DocumentStore. If you want to install only the dependencies needed for model inference on your local hardware (not re…

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. Embeddings databases are a union of vector indexes (sparse and dense), graph networks a…

Open-source offline translation library written in Python

Argos Translate also manages automatically pivoting through intermediate languages to translate between languages that don't have a direct translation between them installed. For example, if you have…

Machine Learning Engineering Online Book

An open collection of methodologies to help with successful training of large language models and multi-modal models. This is a technical material suitable for LLM/VLM training engineers and operator…

Context aware, pluggable and customizable data protection and de-identification SDK for text and images

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provi…

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Does one of these scenarios sounds familiar to you? If so, you are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate …

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch The content of this document is organized as follows: Two simpler at…

Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E, Google Cloud AI,HuggingGPT, and more

Last updated: November 19,2023 Just look for the Dolphin 🐬 and enjoy your ride into the future of AI! To run these notebooks on a cloud platform, just click on one of the badges in the table below or…

Learn Cloud Applied Generative AI Engineering (GenEng) using OpenAI, Gemini, Streamlit, Containers, Serverless, Postgres, LangChain, Pinecone, and Next.js

Note: If possible register your account with a company email address. Once you have a subscription id apply for Azure Open AI Service here: GenEng revolution being led by developers who build deep pr…

Repository for Project Insight: NLP as a Service

The downstream NLP tasks covered: News Classification Entity Recognition Sentiment Analysis Summarization The user can select different models from the drop down to run the inference. The users can a…

Amazon SageMaker Local Mode Examples

This repository contains examples and related resources showing you how to preprocess, train, debug your training script with breakpoints, and serve on your local machine using Amazon SageMaker Local…

Deploy-BERT-for-Sentiment-Analysis-with-FastAPI

Deploy BERT for Sentiment Analysis as REST API using FastAPI, Transformers by Hugging Face and PyTorch

Deploy a pre-trained BERT model for Sentiment Analysis as a REST API using FastAPI The model is trained to classify sentiment (negative, neutral, and positive) on a custom dataset from app reviews on…

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Announcement video When I started to get into more advanced Machine Learning, I started to see how these famous neural network architectures(such as EfficientNet), were doing amazing things. However,…

DECIMER: Deep Learning for Chemical Image Recognition using Efficient-Net V2 + Transformer

The DECIMER 2.2 [5] (Deep lEarning for Chemical ImagE Recognition) project [1] was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open…

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a han…

Easiest way of fine-tuning HuggingFace video classification models

and supports: Tensorboard tracker is enabled by default. To add Neptune/Layer ... tracking: Before opening a PR:

🧐SwanLab: a tool for your machine learning log tracking and experiment management. Join our WeChat ⬇️

[24/01/25] 😄 We supported a new Config/Summary table component that supports parameter search. Additionally, we've used new fonts and color schemes.（v0.1.6） Requirement：Python 3.8+. For example, We w…

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

Most of the previous works in visual understanding, rely solely on understanding the "what" (e.g., object recognition) and "where" (e.g., event localization), which in some cases, fails to describe…

Text Classification in Turkish Texts with Bert

Goal= Correctly guessing the classification of texts and audios It is a text classification task implementation transformers (by HuggingFace) with BERT. It contains several parts: --Data pre-processi…