emotional-distilbert-3
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1515
- Accuracy: 0.4428
- F1: 0.4258
- Precision: 0.4408
- Recall: 0.4428
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
2.7857 | 1.0 | 270 | 2.7060 | 0.2505 | 0.2301 | 0.3881 | 0.2505 |
1.4183 | 2.0 | 540 | 2.1665 | 0.3693 | 0.3674 | 0.4295 | 0.3693 |
0.621 | 3.0 | 810 | 1.8691 | 0.4419 | 0.4343 | 0.4545 | 0.4419 |
0.2352 | 4.0 | 1080 | 1.8406 | 0.4401 | 0.4333 | 0.4571 | 0.4401 |
0.0816 | 5.0 | 1350 | 1.9892 | 0.4474 | 0.4335 | 0.4518 | 0.4474 |
0.0284 | 6.0 | 1620 | 2.1080 | 0.4347 | 0.4224 | 0.4365 | 0.4347 |
0.0141 | 7.0 | 1890 | 2.1515 | 0.4428 | 0.4258 | 0.4408 | 0.4428 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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