---
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-srl_arg
  results: []
---


# bert-base-uncased-finetuned-srl_arg

This model is a baseline 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: 0.1094
- Precision: 0.8207
- Recall: 0.8310
- F1: 0.8259
- Accuracy: 0.9722

## Model description

The appraoch used for the baseline model is basically converting the sentence into the following form:
> [CLS] This is the sentence content [SEP] is [SEP].

And this is realized by simply using the logic of the auto tokenizer: `tokenizer(list1,list2)` will return [CLS] list1 content [SEP] list2 content [SEP].

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1082        | 1.0   | 2655 | 0.1236          | 0.7783    | 0.8158 | 0.7966 | 0.9671   |
| 0.0772        | 2.0   | 5310 | 0.1089          | 0.8055    | 0.8277 | 0.8165 | 0.9708   |
| 0.0609        | 3.0   | 7965 | 0.1094          | 0.8207    | 0.8310 | 0.8259 | 0.9722   |


### Framework versions

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