distilbert_finetune_own_data_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0618
- Precision: 0.8889
- Recall: 0.8889
- F1: 0.8889
- Accuracy: 0.9773
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 23 | 0.3117 | 1.0 | 0.6667 | 0.8 | 0.9091 |
| No log | 2.0 | 46 | 0.1638 | 0.7778 | 0.7778 | 0.7778 | 0.9318 |
| No log | 3.0 | 69 | 0.1322 | 0.875 | 0.7778 | 0.8235 | 0.9545 |
| No log | 4.0 | 92 | 0.0582 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 5.0 | 115 | 0.1196 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 6.0 | 138 | 0.0607 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 7.0 | 161 | 0.0918 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 8.0 | 184 | 0.0512 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 9.0 | 207 | 0.0521 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
| No log | 10.0 | 230 | 0.0618 | 0.8889 | 0.8889 | 0.8889 | 0.9773 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for senthil2002/distilbert_finetune_own_data_model
Base model
distilbert/distilbert-base-uncased