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 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
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