FinBERT-QAFinancial Domain Question Answering with pre-trained BERT Language Model
Stars: ✭ 70 (+268.42%)
Mutual labels: information-retrieval, question-answering, bert
Haystack🔍 Haystack is an open source NLP framework that leverages Transformer models. It enables developers to implement production-ready neural search, question answering, semantic document search and summarization for a wide range of applications.
Stars: ✭ 3,409 (+17842.11%)
Mutual labels: information-retrieval, question-answering, bert
text2textText2Text: Cross-lingual natural language processing and generation toolkit
Stars: ✭ 188 (+889.47%)
Mutual labels: information-retrieval, question-answering, bert
cmrc2019A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (CMRC 2019)
Stars: ✭ 118 (+521.05%)
Mutual labels: question-answering, reading-comprehension, bert
backpropBackprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
Stars: ✭ 229 (+1105.26%)
Mutual labels: question-answering, bert
HARCode for WWW2019 paper "A Hierarchical Attention Retrieval Model for Healthcare Question Answering"
Stars: ✭ 22 (+15.79%)
Mutual labels: information-retrieval, question-answering
Cross-Lingual-MRCCross-Lingual Machine Reading Comprehension (EMNLP 2019)
Stars: ✭ 66 (+247.37%)
Mutual labels: reading-comprehension, bert
extractive rc by runtime mtCode and datasets of "Multilingual Extractive Reading Comprehension by Runtime Machine Translation"
Stars: ✭ 36 (+89.47%)
Mutual labels: question-answering, reading-comprehension
ProQAProgressively Pretrained Dense Corpus Index for Open-Domain QA and Information Retrieval
Stars: ✭ 44 (+131.58%)
Mutual labels: information-retrieval, question-answering
co-attentionPytorch implementation of "Dynamic Coattention Networks For Question Answering"
Stars: ✭ 54 (+184.21%)
Mutual labels: question-answering, reading-comprehension
BERT-QECode and resources for the paper "BERT-QE: Contextualized Query Expansion for Document Re-ranking".
Stars: ✭ 43 (+126.32%)
Mutual labels: information-retrieval, bert
exams-qaA Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering
Stars: ✭ 25 (+31.58%)
Mutual labels: question-answering, reading-comprehension
KitanaQAKitanaQA: Adversarial training and data augmentation for neural question-answering models
Stars: ✭ 58 (+205.26%)
Mutual labels: question-answering, bert
TriB-QA吹逼我们是认真的
Stars: ✭ 45 (+136.84%)
Mutual labels: question-answering, bert
explicit memory tracker[ACL 2020] Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Stars: ✭ 35 (+84.21%)
Mutual labels: question-answering, reading-comprehension
PersianQAPersian (Farsi) Question Answering Dataset (+ Models)
Stars: ✭ 114 (+500%)
Mutual labels: question-answering, reading-comprehension
SQUAD2.Q-Augmented-DatasetAugmented version of SQUAD 2.0 for Questions
Stars: ✭ 31 (+63.16%)
Mutual labels: question-answering, bert
cherche📑 Neural Search
Stars: ✭ 196 (+931.58%)
Mutual labels: information-retrieval, question-answering
beirA Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Stars: ✭ 738 (+3784.21%)
Mutual labels: information-retrieval, bert