transformers pytorch 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-mnli-model-bert-base-uncased-run-3
  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-mnli-model-bert-base-uncased-run-3

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: 1.3201
- Accuracy: 0.8013

## 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: 45
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.6829        | 0.16  | 2000  | 2.2880          | 0.7303   |
| 2.2547        | 0.33  | 4000  | 2.0121          | 0.7413   |
| 2.0198        | 0.49  | 6000  | 1.7345          | 0.7652   |
| 1.9401        | 0.65  | 8000  | 1.6183          | 0.7737   |
| 1.7915        | 0.81  | 10000 | 1.5497          | 0.7737   |
| 1.7634        | 0.98  | 12000 | 1.4988          | 0.7833   |
| 1.3585        | 1.14  | 14000 | 1.5079          | 0.7817   |
| 1.362         | 1.3   | 16000 | 1.4842          | 0.7802   |
| 1.3598        | 1.47  | 18000 | 1.4116          | 0.7893   |
| 1.3384        | 1.63  | 20000 | 1.4132          | 0.7904   |
| 1.3822        | 1.79  | 22000 | 1.4139          | 0.7873   |
| 1.3326        | 1.96  | 24000 | 1.4238          | 0.7937   |
| 1.0299        | 2.12  | 26000 | 1.3574          | 0.7930   |
| 1.0497        | 2.28  | 28000 | 1.3852          | 0.7970   |
| 1.0439        | 2.44  | 30000 | 1.3526          | 0.7953   |
| 1.0561        | 2.61  | 32000 | 1.3424          | 0.7966   |
| 1.072         | 2.77  | 34000 | 1.3665          | 0.7977   |
| 1.0487        | 2.93  | 36000 | 1.3604          | 0.8017   |
| 0.882         | 3.1   | 38000 | 1.3649          | 0.7990   |
| 0.8851        | 3.26  | 40000 | 1.3544          | 0.7964   |
| 0.8969        | 3.42  | 42000 | 1.3558          | 0.7952   |
| 0.9023        | 3.59  | 44000 | 1.3260          | 0.8004   |
| 0.9136        | 3.75  | 46000 | 1.3244          | 0.8039   |
| 0.9172        | 3.91  | 48000 | 1.3003          | 0.8023   |
| 0.7695        | 4.07  | 50000 | 1.2861          | 0.7997   |
| 0.782         | 4.24  | 52000 | 1.2736          | 0.7997   |
| 0.7903        | 4.4   | 54000 | 1.3315          | 0.8002   |
| 0.7948        | 4.56  | 56000 | 1.3201          | 0.8013   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.5
- Tokenizers 0.13.3
