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OTE-ABSA-Qarib-DAPT-LABR-run1
This model is a fine-tuned version of salohnana2018/Qarib-domianAdaption-OTE-ABSA-LABR on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1353
- Precision: 0.7645
- Recall: 0.7762
- F1: 0.7703
- Accuracy: 0.9509
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: 5e-05
- train_batch_size: 32
- 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 |
---|---|---|---|---|---|---|---|
0.2003 | 1.0 | 121 | 0.1354 | 0.7193 | 0.7970 | 0.7562 | 0.9461 |
0.1047 | 2.0 | 242 | 0.1262 | 0.8056 | 0.7213 | 0.7611 | 0.9519 |
0.0754 | 3.0 | 363 | 0.1353 | 0.7645 | 0.7762 | 0.7703 | 0.9509 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3