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-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-SCR-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: 1.3176
- Accuracy: 0.8930

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.9067        | 0.18  | 2000  | 2.3877          | 0.8405   |
| 2.4757        | 0.35  | 4000  | 1.9083          | 0.8599   |
| 2.2477        | 0.53  | 6000  | 1.9314          | 0.8583   |
| 2.0701        | 0.7   | 8000  | 1.6292          | 0.8708   |
| 1.9984        | 0.88  | 10000 | 1.7295          | 0.8731   |
| 1.4555        | 1.06  | 12000 | 1.5185          | 0.8795   |
| 1.4563        | 1.23  | 14000 | 1.6468          | 0.8762   |
| 1.4602        | 1.41  | 16000 | 1.4292          | 0.8805   |
| 1.3895        | 1.58  | 18000 | 1.4390          | 0.8821   |
| 1.445         | 1.76  | 20000 | 1.6081          | 0.8802   |
| 1.3932        | 1.93  | 22000 | 1.4188          | 0.8866   |
| 1.0049        | 2.11  | 24000 | 1.4497          | 0.8866   |
| 1.0289        | 2.29  | 26000 | 1.3805          | 0.8884   |
| 1.0486        | 2.46  | 28000 | 1.4963          | 0.8861   |
| 1.0587        | 2.64  | 30000 | 1.4149          | 0.8886   |
| 1.0485        | 2.81  | 32000 | 1.2935          | 0.8889   |
| 1.0582        | 2.99  | 34000 | 1.2779          | 0.8895   |
| 0.7855        | 3.17  | 36000 | 1.2838          | 0.8908   |
| 0.8174        | 3.34  | 38000 | 1.3120          | 0.8900   |
| 0.8416        | 3.52  | 40000 | 1.3620          | 0.8907   |
| 0.8425        | 3.69  | 42000 | 1.4111          | 0.8918   |
| 0.8628        | 3.87  | 44000 | 1.3038          | 0.8917   |
| 0.684         | 4.05  | 46000 | 1.3762          | 0.8938   |
| 0.7077        | 4.22  | 48000 | 1.3890          | 0.8906   |
| 0.7155        | 4.4   | 50000 | 1.2768          | 0.8946   |
| 0.6884        | 4.57  | 52000 | 1.3716          | 0.8923   |
| 0.7467        | 4.75  | 54000 | 1.3551          | 0.8898   |
| 0.7278        | 4.93  | 56000 | 1.3059          | 0.8926   |
| 0.6269        | 5.1   | 58000 | 1.2444          | 0.8937   |
| 0.6319        | 5.28  | 60000 | 1.2887          | 0.8951   |
| 0.6215        | 5.45  | 62000 | 1.3326          | 0.8940   |
| 0.6293        | 5.63  | 64000 | 1.2623          | 0.8946   |
| 0.6474        | 5.8   | 66000 | 1.4550          | 0.8927   |
| 0.6604        | 5.98  | 68000 | 1.2514          | 0.8951   |
| 0.544         | 6.16  | 70000 | 1.3176          | 0.8930   |


### Framework versions

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