suamin/T2NER
T2NER: Transformers based Transfer Learning Framework for Named Entity Recognition (EACL 2021)
This framework helps developers build systems that can automatically identify and categorize key entities like names, locations, or organizations within text. It takes raw text data (like news articles or reports) and outputs the same text with specific words or phrases tagged with their entity types. It's used by natural language processing engineers and researchers who need to develop or improve text analysis applications.
No commits in the last 6 months.
Use this if you are an NLP developer or researcher looking to apply transfer learning with transformer models for named entity recognition tasks, especially across different languages.
Not ideal if you need a pre-built, ready-to-use application for named entity recognition without any programming or model training.
Stars
11
Forks
5
Language
Python
License
MIT
Category
Last pushed
Sep 24, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/suamin/T2NER"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deeppavlov/AutoIntent
Automated machine learning for text classification
shushanxingzhe/transformers_ner
Add CRF or LSTM+CRF for huggingface transformers bert to perform better on NER task. It is very...
fran-martinez/bio_ner_bert
BERT finetuned on NER downstream tasks
liuyukid/transformers-ner
Pytorch-Named-Entity-Recognition-with-transformers
dsindex/iclassifier
reference pytorch code for intent classification