scenario-TCR-data-glue-qnli-model-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4691
- Accuracy: 0.8885
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: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.303 | 1.0 | 3273 | 0.2478 | 0.9023 |
| 0.2095 | 2.0 | 6546 | 0.2681 | 0.9033 |
| 0.1365 | 3.0 | 9819 | 0.3354 | 0.8993 |
| 0.1107 | 4.0 | 13093 | 0.3548 | 0.8971 |
| 0.0963 | 5.0 | 16366 | 0.4401 | 0.8922 |
| 0.0862 | 6.0 | 19639 | 0.4351 | 0.8876 |
| 0.0818 | 7.0 | 22912 | 0.4691 | 0.8885 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
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