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-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-PRE-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: 1.2044
- Accuracy: 0.8968

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 2.931         | 0.18  | 2000   | 2.2665          | 0.8432   |
| 2.4959        | 0.35  | 4000   | 1.8627          | 0.8608   |
| 2.2588        | 0.53  | 6000   | 1.9148          | 0.8587   |
| 2.0673        | 0.7   | 8000   | 1.6430          | 0.8708   |
| 2.028         | 0.88  | 10000  | 1.6752          | 0.8628   |
| 1.4394        | 1.06  | 12000  | 1.5353          | 0.8796   |
| 1.4491        | 1.23  | 14000  | 1.4747          | 0.8806   |
| 1.4069        | 1.41  | 16000  | 1.4089          | 0.8822   |
| 1.3913        | 1.58  | 18000  | 1.4139          | 0.8812   |
| 1.4544        | 1.76  | 20000  | 1.3681          | 0.8846   |
| 1.4392        | 1.93  | 22000  | 1.3060          | 0.8868   |
| 0.993         | 2.11  | 24000  | 1.4723          | 0.8847   |
| 1.0377        | 2.29  | 26000  | 1.3646          | 0.8852   |
| 1.035         | 2.46  | 28000  | 1.3723          | 0.8875   |
| 1.0906        | 2.64  | 30000  | 1.4003          | 0.8891   |
| 1.0464        | 2.81  | 32000  | 1.3512          | 0.8909   |
| 1.0883        | 2.99  | 34000  | 1.3431          | 0.8911   |
| 0.7843        | 3.17  | 36000  | 1.2574          | 0.8873   |
| 0.8255        | 3.34  | 38000  | 1.2612          | 0.8912   |
| 0.8484        | 3.52  | 40000  | 1.2567          | 0.8912   |
| 0.8514        | 3.69  | 42000  | 1.3091          | 0.8881   |
| 0.857         | 3.87  | 44000  | 1.3303          | 0.8906   |
| 0.6519        | 4.05  | 46000  | 1.2750          | 0.8883   |
| 0.7005        | 4.22  | 48000  | 1.2341          | 0.8928   |
| 0.6878        | 4.4   | 50000  | 1.3843          | 0.8901   |
| 0.7183        | 4.57  | 52000  | 1.2741          | 0.8927   |
| 0.7382        | 4.75  | 54000  | 1.2390          | 0.8914   |
| 0.7406        | 4.93  | 56000  | 1.2676          | 0.8898   |
| 0.6024        | 5.1   | 58000  | 1.3540          | 0.8930   |
| 0.615         | 5.28  | 60000  | 1.1930          | 0.8925   |
| 0.6134        | 5.45  | 62000  | 1.2395          | 0.8912   |
| 0.6589        | 5.63  | 64000  | 1.2757          | 0.8933   |
| 0.6318        | 5.8   | 66000  | 1.3208          | 0.8926   |
| 0.6427        | 5.98  | 68000  | 1.2758          | 0.8940   |
| 0.5585        | 6.16  | 70000  | 1.1598          | 0.8913   |
| 0.569         | 6.33  | 72000  | 1.1830          | 0.8936   |
| 0.5974        | 6.51  | 74000  | 1.2108          | 0.8914   |
| 0.5853        | 6.68  | 76000  | 1.3064          | 0.8924   |
| 0.6016        | 6.86  | 78000  | 1.2944          | 0.8941   |
| 0.5589        | 7.04  | 80000  | 1.1880          | 0.8933   |
| 0.519         | 7.21  | 82000  | 1.1704          | 0.8957   |
| 0.5289        | 7.39  | 84000  | 1.2815          | 0.8927   |
| 0.5266        | 7.56  | 86000  | 1.2267          | 0.8954   |
| 0.551         | 7.74  | 88000  | 1.2894          | 0.8944   |
| 0.5554        | 7.92  | 90000  | 1.3132          | 0.8958   |
| 0.4909        | 8.09  | 92000  | 1.1937          | 0.8961   |
| 0.4973        | 8.27  | 94000  | 1.1548          | 0.8969   |
| 0.4778        | 8.44  | 96000  | 1.2405          | 0.8947   |
| 0.5022        | 8.62  | 98000  | 1.3151          | 0.8962   |
| 0.5102        | 8.79  | 100000 | 1.2093          | 0.8955   |
| 0.5061        | 8.97  | 102000 | 1.2177          | 0.8953   |
| 0.4564        | 9.15  | 104000 | 1.2044          | 0.8968   |


### Framework versions

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