marthuis

marthuis's Stars

• gruns/icecream

  🍦 Never use print() to debug again.

  Language:Python9.3k191

• guidance-ai/guidance

  A guidance language for controlling large language models.

  Language:Jupyter Notebook19.3k1k

• squat/drae

  A RESTful API for el Diccionario de la Real Academia Española

  Language:Go6619

• PySimpleGUI/PySimpleGUI

  Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.

  Language:Python13.5k1.8k

• allenai/scientific-claim-generation

  Generating claims for zero-shot scientific fact checking

  Language:Python293

• nlpfromscratch/nlp-llms-resources

  Master list of curated resources on NLP and LLMs

  11022

• hltfbk/E3C-Corpus

  E3C is a freely available multilingual corpus (Italian, English, French, Spanish, and Basque) of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, and training of information extraction systems. It consists of two types of annotations: (i) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (i.e. SNOMED-CT, ICD-10); and (ii) temporal information and factuality: events, time expressions, and temporal relations according to the THYME standard. The corpus is organised into three layers, with different purposes. Layer 1: about 25K tokens per language with full manual annotation of clinical entities, temporal information and factuality, for benchmarkingand linguistic analysis. Layer 2: 50-100K tokens per language with semi-automatic annotations of clinical entities, to be used to train baseline systems. Layer 3: about 1M tokens per language of non-annotated medical documents to be exploited by semi-supervised approaches. Researchers can use the benchmark training and test splits of our corpus to develop and test their own models. We trained several deep learning based models and provide baselines using the benchmark. Both the corpus and the built models will be available through the ELG platform.

  243

• facebookresearch/DPR

  Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

  Language:Python1.7k306

• getalp/wikIR

  A python tool for building large scale Wikipedia-based Information Retrieval datasets

  Language:Python457

• attardi/wikiextractor

  A tool for extracting plain text from Wikipedia dumps

  Language:Python3.8k966

• harpribot/awesome-information-retrieval

  A curated list of awesome information retrieval resources

  1.1k138

• allenai/scifact

  Data and models for the SciFact verification task.

  Language:Python22525

• wikifactcheck-english/wikifactcheck-english

  Data and download script to accompany LREC2020 paper "Automated Fact-Checking of Claims from Wikipedia"

  Language:Python131

• wikifactcheck-english/wfc-en-crawl

  repository housing web-crawling and scraping code for WikiFactCheck-en evidence

  1

• jiho283/FactKG

  Official repository of FactKG

  Language:Python537

• orai-nlp/SpanishGLUE

  Spanish NLU Evaluation Framework / Marco de Evaluación para NLU en Castellano

  2

• allenai/scitail

  Given a pair of sentences (premise, hypothesis), the decomposed graph entailment model (DGEM) predicts whether the premise can be used to infer the hypothesis.

  Language:Python5310

• kay-wong/Wiki-Reliability

  Wiki-Reliability: A Large Scale Dataset for Content Reliability on Wikipedia

  Language:Jupyter Notebook91

• artetxem/esxnli

  A bilingual NLI dataset annotated in Spanish and human translated into English

  84

• XInfoTabS/dataset

  The Official dataset for "XINFOTABS: Evaluating Multilingual Tabular Natural Language Inference", containing tables and corresponding hypothesis in 10 languages.

  Language:Python3

• facebookresearch/XNLI

  Evaluating Cross-lingual Sentence Representations

  44444

• salesforce/factCC

  Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

  Language:Python28831

• allenai/gooaq

  Question-answers, collected from Google

  Language:Python12412

• allenai/OpenBookQA

  Code for experiments on OpenBookQA from the EMNLP 2018 paper "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering"

  Language:Python12330

• hadyelsahar/RE-NLG-Dataset

  T-Rex : A Large Scale Alignment of Natural Language with Knowledge Base Triples

  Language:Python6412

• StonyBrookNLP/musique

  Repository for MuSiQue: Multi-hop Questions via Single-hop Question Composition, TACL 2022

  Language:Python1007

• promptfoo/promptfoo

  Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

  Language:TypeScript5k402

• google-research/true

  Code and data accompanying the paper "TRUE: Re-evaluating Factual Consistency Evaluation".

  Language:Python7110

• cambridge-wtwt/emnlp2020-stander-news

  5

• explosion/spacy-llm

  🦙 Integrating LLMs into structured NLP pipelines

  Language:Python1.1k90