metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-negation_scope
results: []
datasets:
- dannashao/sem2012forNegbert
language:
- en
metrics:
- dannashao/span_metric
pipeline_tag: token-classification
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
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Token Precision | Token Recall | Token F1 | Span Precision | Span Recall | Span F1 |
|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 237 | 0.0624 | 0.9121 | 0.8368 | 0.8728 | 0.5207 | 0.5207 | 0.5207 |
| No log | 2.0 | 474 | 0.0682 | 0.9366 | 0.8311 | 0.8807 | 0.6012 | 0.6012 | 0.6012 |
| 0.0722 | 3.0 | 711 | 0.0618 | 0.9190 | 0.8868 | 0.9026 | 0.625 | 0.625 | 0.625 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1