Named Entities Recognition by Token Pair Linking
Project description
NestedNER
Prerequisites
The main requirements are:
- tqdm
- flair
- word2vec
- glove-python-binary==0.2.0
- transformers==2.10.0
- wandb # for logging the results
- yaml
In the root directory, run
pip install -e .
Data Format
{
"id": "train_2975",
"text": "ll Services Plan Documents Only ( Section 125 ) Premium Offset Plan ( POP ) Wellness Plans MBTA Corporate Transit Pass Program WMATA Smar Trip Transit / Parking Program Contact Us HRC Total Solutions 111 Charles Street Manchester , New hampshire 03101 customerservice @ hrcts . com Phone : ( 603 ) 647 - 1147 Fax : ( 866 ) 978 - 7868 Follow Us Linked In You Tube Twitter Resources IIAS Participating Pharmacies IIAS 90 % Merchant List FSA Store HSA St",
"entity_list": [
{"text": "111 Charles Street", "type": "detail", "char_span": [200, 218]},
{"text": "Manchester", "type": "city", "char_span": [219, 229]},
{"text": "New hampshire", "type": "state", "char_span": [232, 245]},
{"text": "03101", "type": "zipcode", "char_span": [246, 251]},
]
}
Pretrained Model and Word Embeddings
Download BERT-BASE-CASED and put it under ../pretrained_models
. Download word embeddings (code: 8044) and put them under ../pretrained_emb
.
Train
Set configuration in tplinker_ner/train_config.yaml
. Start training:
cd tplinker_ner
python train.py
Evaluation
Set configuration in tplinker_ner/eval_config.yaml
. Start evaluation by running tplinker_ner/Evaluation.ipynb
Project details
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