distilled_bert_uncased_steam_review_fined_tune
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: 0.8572
-
Accuracy: 0.6667
-
Confusion Matrix: [[0, 0, 8], [0, 9, 2], [0, 0, 11]]
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Classification Report: precision recall f1-score support
Negative 0.00 0.00 0.00 8
Neutral 1.00 0.82 0.90 11
Positive 0.52 1.00 0.69 11
accuracy 0.67 30
macro avg 0.51 0.61 0.53 30
weighted avg 0.56 0.67 0.58 30
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
|---|
| No log | 1.0 | 5 | 0.9702 | 0.6667 | [[0, 0, 8], [0, 9, 2], [0, 0, 11]] | precision recall f1-score support |
Negative 0.00 0.00 0.00 8
Neutral 1.00 0.82 0.90 11
Positive 0.52 1.00 0.69 11
accuracy 0.67 30
macro avg 0.51 0.61 0.53 30
weighted avg 0.56 0.67 0.58 30
|
| No log | 2.0 | 10 | 0.9022 | 0.6667 | [[0, 0, 8], [0, 9, 2], [0, 0, 11]] | precision recall f1-score support
Negative 0.00 0.00 0.00 8
Neutral 1.00 0.82 0.90 11
Positive 0.52 1.00 0.69 11
accuracy 0.67 30
macro avg 0.51 0.61 0.53 30
weighted avg 0.56 0.67 0.58 30
|
| No log | 3.0 | 15 | 0.8572 | 0.6667 | [[0, 0, 8], [0, 9, 2], [0, 0, 11]] | precision recall f1-score support
Negative 0.00 0.00 0.00 8
Neutral 1.00 0.82 0.90 11
Positive 0.52 1.00 0.69 11
accuracy 0.67 30
macro avg 0.51 0.61 0.53 30
weighted avg 0.56 0.67 0.58 30
|
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
