|
--- |
|
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 |