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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: scenario-KD-SCR-DIV2-data-glue-qnli-model-bert-base-uncased-run-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-KD-SCR-DIV2-data-glue-qnli-model-bert-base-uncased-run-1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7514
- Accuracy: 0.8627
## 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: 32
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.4263 | 1.0 | 3273 | 1.6907 | 0.8545 |
| 1.7748 | 2.0 | 6547 | 1.8491 | 0.8499 |
| 1.1414 | 3.0 | 9820 | 1.9422 | 0.8545 |
| 0.8965 | 4.0 | 13094 | 1.7533 | 0.8552 |
| 0.7756 | 5.0 | 16367 | 1.7103 | 0.8570 |
| 0.6527 | 6.0 | 19641 | 1.6665 | 0.8569 |
| 0.6056 | 7.0 | 22914 | 1.5879 | 0.8620 |
| 0.5559 | 8.0 | 26188 | 1.6570 | 0.8618 |
| 0.5154 | 9.0 | 29461 | 1.5519 | 0.8658 |
| 0.4752 | 10.0 | 32735 | 1.6905 | 0.8612 |
| 0.4581 | 11.0 | 36008 | 1.6075 | 0.8644 |
| 0.4322 | 12.0 | 39282 | 1.6963 | 0.8614 |
| 0.3969 | 13.0 | 42555 | 1.6467 | 0.8660 |
| 0.393 | 14.0 | 45829 | 1.6735 | 0.8680 |
| 0.3651 | 15.0 | 49102 | 1.7631 | 0.8614 |
| 0.3464 | 16.0 | 52376 | 1.7957 | 0.8645 |
| 0.3455 | 17.0 | 55649 | 1.7008 | 0.8680 |
| 0.3276 | 18.0 | 58923 | 1.7183 | 0.8669 |
| 0.3239 | 19.0 | 62196 | 1.7514 | 0.8627 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
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