transformers safetensors bert generated_from_trainer base_model:bert-base-uncased license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: scenario-KD-PRE-DIV2-data-glue-qqp-model-bert-base-uncased-run-2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-PRE-DIV2-data-glue-qqp-model-bert-base-uncased-run-2

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: 1.2988
- Accuracy: 0.8923

## 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: 43
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.9055        | 0.18  | 2000  | 2.4111          | 0.8392   |
| 2.4325        | 0.35  | 4000  | 1.9707          | 0.8568   |
| 2.2685        | 0.53  | 6000  | 1.8097          | 0.8611   |
| 2.0654        | 0.7   | 8000  | 1.6192          | 0.8716   |
| 2.0215        | 0.88  | 10000 | 2.0656          | 0.8647   |
| 1.4466        | 1.06  | 12000 | 1.5295          | 0.8764   |
| 1.4517        | 1.23  | 14000 | 1.5509          | 0.8796   |
| 1.4621        | 1.41  | 16000 | 1.5028          | 0.8772   |
| 1.4127        | 1.58  | 18000 | 1.4584          | 0.8808   |
| 1.4401        | 1.76  | 20000 | 1.3747          | 0.8838   |
| 1.3835        | 1.93  | 22000 | 1.3902          | 0.8849   |
| 0.9922        | 2.11  | 24000 | 1.4736          | 0.8869   |
| 1.0115        | 2.29  | 26000 | 1.3396          | 0.8864   |
| 1.0346        | 2.46  | 28000 | 1.4620          | 0.8850   |
| 1.0638        | 2.64  | 30000 | 1.4527          | 0.8848   |
| 1.0582        | 2.81  | 32000 | 1.3463          | 0.8872   |
| 1.0441        | 2.99  | 34000 | 1.3256          | 0.8872   |
| 0.7995        | 3.17  | 36000 | 1.2894          | 0.8889   |
| 0.807         | 3.34  | 38000 | 1.2997          | 0.8902   |
| 0.8816        | 3.52  | 40000 | 1.2748          | 0.8863   |
| 0.8344        | 3.69  | 42000 | 1.4763          | 0.8907   |
| 0.8383        | 3.87  | 44000 | 1.3446          | 0.8897   |
| 0.6866        | 4.05  | 46000 | 1.3053          | 0.8909   |
| 0.6905        | 4.22  | 48000 | 1.2684          | 0.8921   |
| 0.6909        | 4.4   | 50000 | 1.3176          | 0.8890   |
| 0.7049        | 4.57  | 52000 | 1.2767          | 0.8911   |
| 0.7195        | 4.75  | 54000 | 1.3130          | 0.8848   |
| 0.7329        | 4.93  | 56000 | 1.3751          | 0.8932   |
| 0.5959        | 5.1   | 58000 | 1.4344          | 0.8932   |
| 0.6122        | 5.28  | 60000 | 1.2461          | 0.8895   |
| 0.6352        | 5.45  | 62000 | 1.2165          | 0.8926   |
| 0.6617        | 5.63  | 64000 | 1.3775          | 0.8911   |
| 0.6615        | 5.8   | 66000 | 1.2988          | 0.8923   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
