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-SCR-DIV2-data-glue-qqp-model-bert-base-uncased-run-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-SCR-DIV2-data-glue-qqp-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: 1.1856
- Accuracy: 0.8950

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.9556        | 0.18  | 2000  | 2.3016          | 0.8419   |
| 2.5167        | 0.35  | 4000  | 1.9944          | 0.8552   |
| 2.2674        | 0.53  | 6000  | 1.8752          | 0.8633   |
| 2.0376        | 0.7   | 8000  | 1.6815          | 0.8721   |
| 2.0109        | 0.88  | 10000 | 1.5454          | 0.8702   |
| 1.4606        | 1.06  | 12000 | 1.5022          | 0.8764   |
| 1.4567        | 1.23  | 14000 | 1.4141          | 0.8812   |
| 1.3994        | 1.41  | 16000 | 1.4605          | 0.8825   |
| 1.4263        | 1.58  | 18000 | 1.5578          | 0.8753   |
| 1.4554        | 1.76  | 20000 | 1.5297          | 0.8828   |
| 1.4177        | 1.93  | 22000 | 1.2702          | 0.8880   |
| 0.9997        | 2.11  | 24000 | 1.4059          | 0.8800   |
| 1.0285        | 2.29  | 26000 | 1.4210          | 0.8831   |
| 1.0676        | 2.46  | 28000 | 1.4104          | 0.8878   |
| 1.0946        | 2.64  | 30000 | 1.3059          | 0.8875   |
| 1.0212        | 2.81  | 32000 | 1.3785          | 0.8889   |
| 1.0913        | 2.99  | 34000 | 1.3817          | 0.8875   |
| 0.7837        | 3.17  | 36000 | 1.2574          | 0.8910   |
| 0.8305        | 3.34  | 38000 | 1.2687          | 0.8898   |
| 0.8374        | 3.52  | 40000 | 1.3526          | 0.8893   |
| 0.8324        | 3.69  | 42000 | 1.3256          | 0.8885   |
| 0.8692        | 3.87  | 44000 | 1.2419          | 0.8914   |
| 0.6698        | 4.05  | 46000 | 1.2721          | 0.8918   |
| 0.7247        | 4.22  | 48000 | 1.2845          | 0.8918   |
| 0.6894        | 4.4   | 50000 | 1.3141          | 0.8894   |
| 0.7079        | 4.57  | 52000 | 1.3442          | 0.8919   |
| 0.7376        | 4.75  | 54000 | 1.2846          | 0.8910   |
| 0.7675        | 4.93  | 56000 | 1.2718          | 0.8939   |
| 0.6049        | 5.1   | 58000 | 1.3554          | 0.8912   |
| 0.6241        | 5.28  | 60000 | 1.2329          | 0.8949   |
| 0.6299        | 5.45  | 62000 | 1.2851          | 0.8956   |
| 0.6305        | 5.63  | 64000 | 1.2549          | 0.8949   |
| 0.6441        | 5.8   | 66000 | 1.3764          | 0.8930   |
| 0.6352        | 5.98  | 68000 | 1.2044          | 0.8945   |
| 0.5705        | 6.16  | 70000 | 1.1740          | 0.8950   |
| 0.5689        | 6.33  | 72000 | 1.2562          | 0.8957   |
| 0.5789        | 6.51  | 74000 | 1.2119          | 0.8948   |
| 0.5976        | 6.68  | 76000 | 1.2916          | 0.8950   |
| 0.5868        | 6.86  | 78000 | 1.2307          | 0.8949   |
| 0.553         | 7.04  | 80000 | 1.1894          | 0.8940   |
| 0.5129        | 7.21  | 82000 | 1.1856          | 0.8950   |


### Framework versions

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