---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-advanced-srl_arg
  results: []
datasets:
- dannashao/UniversalPropBank_ENG
---

# bert-base-uncased-finetuned-advanced-srl_arg

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the English Universal Propbank dataset for the Semantics Role Labeling (SRL) task.
It achieves the following results on the evaluation set:
- Loss: 0.0914
- Precision: 0.8664
- Recall: 0.8673
- F1: 0.8669
- Accuracy: 0.9812

## Model description

This more advanced SRL model uses similar apporach as 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 ([V]) immediately before the predicate:
> This [V] is a sentence.

Note that **the special token and the predicate is considered a whole**. That is, the actual sentence is like
> 'This' **'[V] is'** 'a' 'sentence' '.'

## Usages

The model labels semantics roles given input sentences. See usage examples at https://github.com/dannashao/bertsrl/blob/main/Evaluation.ipynb

## Training and evaluation data

The English Universal Proposition Bank v1.0 data. See details at https://github.com/UniversalPropositions/UP-1.0

## Training procedure

See details at https://github.com/chuqiaog/Advanced_NLP_group_1/blob/main/A3/A3_main.ipynb

### 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0457        | 1.0   | 2655 | 0.0849          | 0.8447    | 0.8644 | 0.8544 | 0.9792   |
| 0.0322        | 2.0   | 5310 | 0.0883          | 0.8586    | 0.8679 | 0.8632 | 0.9806   |
| 0.0234        | 3.0   | 7965 | 0.0914          | 0.8664    | 0.8673 | 0.8669 | 0.9812   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1