dannashao/bert-base-uncased-finetuned-negation_scope

bert-base-uncased-finetuned-negation_scope

This model is a fine-tuned version of bert-base-uncased on the SEM 2012 shared task corpus (cd-sco). It achieves the following results on the evaluation set:

• —Loss: 0.0618
• —Token Precision: 0.9190
• —Token Recall: 0.8868
• —Token F1: 0.9026
• —Span Precision: 0.625
• —Span Recall: 0.625
• —Span F1: 0.625

Model description

We follow the Augment method described in NegBERT (Khandelwal, et al. 2020). That is, adding a special token ([NEG]) immediately before the predicate:

This is [NEG] not a sentence.

Note that the special token and the predicate is considered a whole. That is, the actual sentence is like

'This' 'is' '[NEG] not' 'a' 'sentence' '.'

Intended uses & limitations

See details at https://github.com/dannashao/portfolio-NLP/blob/main/NEG/Fine%20tune%20BERT.ipynb

Training and evaluation data

See details at https://www.clips.ua.ac.be/sem2012-st-neg/

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3

Training results

Framework versions

• —Transformers 4.37.0
• —Pytorch 2.0.1+cu117
• —Datasets 2.16.1
• —Tokenizers 0.15.1