cooscao/Bert-BiLSTM-CRF-pytorch
bert-bilstm-crf implemented in pytorch for named entity recognition.
This tool helps you automatically identify and extract specific types of entities, like names of people, places, or medical terms, from Chinese text. You input raw Chinese text that has been prepared into a specific 'BIO' format, and the system outputs the same text with the identified entities tagged. This is useful for anyone working with large volumes of Chinese text data, such as researchers, linguists, or data analysts.
283 stars. No commits in the last 6 months.
Use this if you need to precisely extract predefined categories of information from Chinese text data, especially in a specialized domain like healthcare.
Not ideal if you're working with languages other than Chinese or if you don't have text prepared in the 'BIO' annotation format.
Stars
283
Forks
57
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 22, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/cooscao/Bert-BiLSTM-CRF-pytorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Higher-rated alternatives
charles9n/bert-sklearn
a sklearn wrapper for Google's BERT model
jidasheng/bi-lstm-crf
A PyTorch implementation of the BI-LSTM-CRF model.
howl-anderson/seq2annotation
基于 TensorFlow & PaddlePaddle 的通用序列标注算法库（目前包含 BiLSTM+CRF, Stacked-BiLSTM+CRF 和...
guillaumegenthial/tf_ner
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
guillaumegenthial/sequence_tagging
Named Entity Recognition (LSTM + CRF) - Tensorflow