metadata
base_model: qarib/bert-base-qarib
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: OTE-NoDapt-ABSA-bert-base-qarib-OrginalHP-FineTune
results: []
OTE-NoDapt-ABSA-bert-base-qarib-OrginalHP-FineTune
This model is a fine-tuned version of qarib/bert-base-qarib on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1348
- Precision: 0.7488
- Recall: 0.7723
- F1: 0.7604
- Accuracy: 0.9532
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 25
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1656 | 1.0 | 61 | 0.1196 | 0.7299 | 0.7932 | 0.7603 | 0.9528 |
| 0.08 | 2.0 | 122 | 0.1176 | 0.7561 | 0.7678 | 0.7619 | 0.9543 |
| 0.0501 | 3.0 | 183 | 0.1348 | 0.7488 | 0.7723 | 0.7604 | 0.9532 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3