---
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](https://huggingface.co/bert-base-uncased) on the [SEM 2012 shared task](http://www.clips.ua.ac.be/sem2012-st-neg/) 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)](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.704.pdf).
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