Reverse Dependencies of evaluate

The following projects have a declared dependency on evaluate:

• lastmile-eval — An API to measure evaluation criteria (ex: faithfulness) of generative AI outputs
• libvisualwebarena — This is an unofficial, use-at-your-own risks port of the visualwebarena benchmark, for use as a standalone library package.
• libwebarena — This is an unofficial, use-at-your-own risks port of the webarena benchmark, for use as a standalone library package.
• linktransformer — A friendly way to do link, aggregate, cluster and de-duplicate dataframes using large language models.
• llama-recipes — Llama-recipes is a companion project to the Llama models. It's goal is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models.
• llamatune — Haven's Tuning Library for LLM finetuning
• llm-blender — LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths and weaknesses of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
• llm-lens — llm-lens is a Python package for CV as NLP, where you can run very descriptive image modules on images, and then pass those descriptions to a Large Language Model (LLM) to reason about those images.
• llm-serve — An LLM inference solution to quickly deploy productive LLM service
• llm-toolkit — LLM Finetuning resource hub + toolkit
• llm2vec — The official llm2vec library
• llmebench — A Flexible Framework for Accelerating LLMs Benchmarking
• llmsanitize — LLMSanitize: a package to detect contamination in LLMs
• lm-buddy — Ray-centric library for finetuning and evaluation of (large) language models.
• lm-eval — A framework for evaluating language models
• lm-polygraph — Uncertainty Estimation Toolkit for Transformer Language Models
• lmflow-benchmark — LMFlow: Large Model Flow.
• lmflow-deploy — LMFlow: Large Model Flow.
• lmflow-diffusion — LMFlow: Large Model Flow.
• lmflow-eval — LMFlow: Large Model Flow.
• lmflow-evaluate — LMFlow: Large Model Flow.
• lmflow-evaluator — LMFlow: Large Model Flow.
• lmflow-finetune — LMFlow: Large Model Flow.
• lmflow-finetuner — LMFlow: Large Model Flow.
• lmflow-inference — LMFlow: Large Model Flow.
• lmflow-inferencer — LMFlow: Large Model Flow.
• lmflow-pretrain — LMFlow: Large Model Flow.
• lmflow-pretrainer — LMFlow: Large Model Flow.
• lmflow-vision — LMFlow: Large Model Flow.
• lmms-eval — A framework for evaluating large multi-modality language models
• LocalCat — Fine-tune Large Language Models locally.
• luna-nlg — Source code for the LUNA project
• materials-spum-multi — Materials SPUM project.
• mbr — Minimum Bayes risk decoding for Hugging Face Transformers
• metatreelib — PyTorch Implementation for MetaTree: Learning a Decision Tree Algorithm with Transformers
• mindnlp — An open source natural language processing research tool box. Git version: [sha1]:18acd45, [branch]: (HEAD -> master, ms/master)
• mlora — An Efficient Factory to Build Multiple LoRA Adapters
• model-evaluator — Library for beautiful training loop for PyTorch
• model-trainer-app-0.2.0 — A Flask-based model training application with authentication.
• moe-peft — An Efficient LLM Fine-Tuning Factory Optimized for MoE PEFT
• molflux — A foundational package for molecular predictive modelling
• ms-opencompass — A lightweight toolkit for evaluating LLMs based on OpenCompass.
• mtmtrain — no summary
• mygpt — A locally runnable LLM
• Named-Entity-Recognition-BERT-Multilingual-Library-LUX — A comprehensive multilingual Named Entity Recognition (NER) library leveraging BERT. Supports key information extraction tasks across various domains such as biomedical, environmental, and technological.
• navigate-with-image-language-model — The package provides a pipeline that utilizes models like ClipSeg and StableDiffusion or ClipSeg and SegmentAnything to prompt an image for a path.
• nerblackbox — a high-level library for named entity recognition in python
• NewsFrames — Easy-to-use, high-quality identification of generic framing dimensions in English news articles
• nixietune — A semantic search embedding model fine-tuning tool
• nlg-metricverse — An End-to-End Library for Evaluating Natural Language Generation.
• nlpboost — A package for automatic training of NLP (transformers) models
• nlpsig — Path signatures for Natural Language Processing.
• nutmegredundancysolver — no summary
• oarelatedworkevaluator — Package for evaluation of OARelatedWork dataset.
• opencompass — A comprehensive toolkit for large model evaluation
• openicl — An open source framework for in-context learning.
• openicl-nolabel — This is a modified version based on the original openicl 0.1.8 for certain research usages.
• OpenMind — openMind is a magicain who takes you to experience the mystery and creativity of AI.
• openmind-evaluate — The openmind-evaluate is a product which allows you to use evaluate in openMind community.
• openrl — unified reinforcement learning framework
• openseneca — OpenSeneca
• openthaigpt — OpenThaiGPT focuses on developing a Thai Chatbot system to have capabilities equivalent to ChatGPT, as well as being able to connect to external systems and be able to retrieve data flexibly. Easily expandable and customizable and developed into Free open source software for everyone.
• optimum — Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
• optimum-deepsparse — Optimum DeepSparse is an extension of the Hugging Face Transformers library that integrates the DeepSparse inference runtime. DeepSparse offers GPU-class performance on CPUs, making it possible to run Transformers and other deep learning models on commodity hardware with sparsity. Optimum DeepSparse provides a framework for developers to easily integrate DeepSparse into their applications, regardless of the hardware platform.
• optimum-intel — Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.
• pdexplorer — A Stata emulator for Python/pandas
• peal — A package dedicated to using PEFT for active-learning, hence PEAL.
• postocr-3stages — Post-OCR correction in 3 stages.
• prompt2model — A library for distilling models from prompts.
• pt-pump-up — Hub for Portuguese NLP resources
• pykoi — pykoi: Active learning in one unified interface
• pymtlibs — Slepian Scale-Discretised Wavelets in Python
• pyreft — REFT: Representation Finetuning for Language Models
• pythoncharmers-meta — Meta package with dependencies for Python Charmers training courses
• pytranscripts — A python package for extracting electronic health transcripts , and then classifying them based on human annotated data.
• QGEval-metrics — Auto metrics for evaluating generated questions
• QGEval-qg — A tool for question generation based on LMs
• qkogpt — Quantized KoGPT
• qlatent — A Python package for running psychometric on LLMs.
• quasarx — quasar - Pytorch
• query-package-documentation — A package to explore documentations
• RAGchain — Build advanced RAG workflows with LLM, compatible with Langchain
• responsibleai-text — SDK API to assess text Machine Learning models.
• safe-mol — Implementation of the 'Gotta be SAFE: a new framework for molecular design' paper
• saga-llm-evaluation — Versatile Python library designed for evaluating the performance of large language models in Natural Language Processing (NLP) tasks. Developed by Sagacify
• scales-nlp — no summary
• scandeval — Evaluation of pretrained language models on mono- or multilingual language tasks.
• scarabs — scarab: llm training paradigm
• separability — LLM Tools for looking at separability of LLM Capabilities
• seperability — Seperability of LLM Capabilities
• sequence-classifier — no summary
• setfit — Efficient few-shot learning with Sentence Transformers
• sevals — A framework for evaluating language models
• sig-networks — Neural networks for longitudinal NLP classification tasks.
• simple-asr — Wrapper module around wav2vec2 designed for ease of use
• simple-cats — Python package for CATS paper
• singd — KFAC-like Structured Inverse-Free Natural Gradient Descent
• sinhala-llm-evaluator — A module for evaluating language models during training
• span-marker — Named Entity Recognition using Span Markers
• sparseml — Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models