augmented_model_final
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1929
- Accuracy: 0.5697
- F1: 0.5699
- Precision: 0.5731
- Recall: 0.5691
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.9553 | 0.2088 | 500 | 0.7998 | 0.6477 | 0.6332 | 0.6457 | 0.6376 |
0.6974 | 0.4175 | 1000 | 0.7414 | 0.6936 | 0.6792 | 0.6934 | 0.6830 |
0.6307 | 0.6263 | 1500 | 0.7166 | 0.7190 | 0.7076 | 0.7143 | 0.7097 |
0.5916 | 0.8351 | 2000 | 0.7050 | 0.7212 | 0.7126 | 0.7173 | 0.7136 |
0.5517 | 1.0438 | 2500 | 0.7218 | 0.7229 | 0.7148 | 0.7209 | 0.7157 |
0.4939 | 1.2526 | 3000 | 0.7427 | 0.7316 | 0.7220 | 0.7309 | 0.7232 |
0.4823 | 1.4614 | 3500 | 0.7474 | 0.7233 | 0.7143 | 0.7181 | 0.7154 |
0.4771 | 1.6701 | 4000 | 0.7451 | 0.7295 | 0.7198 | 0.7252 | 0.7210 |
0.4649 | 1.8789 | 4500 | 0.7484 | 0.7316 | 0.7222 | 0.7269 | 0.7234 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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