flexudy-pipe Goto Github PK

bloom-fine-tuning

Finetune BLOOM

bloom-lora

Due to restriction of LLaMA, we try to reimplement BLOOM-LoRA (much less restricted BLOOM license here https://huggingface.co/spaces/bigscience/license) using Alpaca-LoRA and Alpaca_data_cleaned.json

chatdoctor

clip

Contrastive Language-Image Pretraining

debatesum

Corresponding code repo for the paper at COLING 2020 - ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset"

docx2python

Extract docx headers, footers, (formatted) text, footnotes, endnotes, properties, and images.

farm

:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

haystack

:mag: Haystack is an open source NLP framework that leverages pre-trained Transformer models. It enables developers to quickly implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications.

involution

[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator

labely

The goal of this project is to provide an easy to use open source tool for data labelling.

mmcv

OpenMMLab Computer Vision Foundation

neural_text_cleaner

Text quality can greatly influence the predictions of neural networks. We atempt to build a model that can do minor cleaning tasks like math, citation and title detection, which can then be masked before any further processing.

nlp-cube

Natural Language Processing Pipeline - Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging and Dependency Parsing

pdf2docx

Parse PDF file with PyMuPDF and generate docx with python-docx

progress

Easy to use progress bars for Python

python-holidays

Generate and work with holidays in Python

pytorch-optimizer

torch-optimizer -- collection of optimizers for Pytorch

qugeev

A simple way to evaluate question generation models

sentence-doctor

Many Natural Language Processing tasks rely on sentence boundary detection (SBD). Although amazing libraries like spacy provide state of the art SBD, they often depend on text extractors (e.g pdf text extractors or OCR). The quality of these extractors greatly influence the quality of SBD libraries and as a consequence, the performance of downstream models as well. To help address this problem, we fine-tuned a T5 model from the hugging face hub that attempts to reconstruct “broken sentences”

spacy-models

💫 Models for the spaCy Natural Language Processing (NLP) library

spn4re

transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

vigogne

Fine-tune French instruction-following models