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---
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 |