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finetuned-token-argumentative
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1573
- Precision: 0.3777
- Recall: 0.3919
- F1: 0.3847
- Accuracy: 0.9497
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 75 | 0.3241 | 0.1109 | 0.2178 | 0.1470 | 0.8488 |
| No log | 2.0 | 150 | 0.3145 | 0.1615 | 0.2462 | 0.1950 | 0.8606 |
| No log | 3.0 | 225 | 0.3035 | 0.1913 | 0.3258 | 0.2411 | 0.8590 |
| No log | 4.0 | 300 | 0.3080 | 0.2199 | 0.3220 | 0.2613 | 0.8612 |
| No log | 5.0 | 375 | 0.3038 | 0.2209 | 0.3277 | 0.2639 | 0.8630 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
