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-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-mnli-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: 1.3373
- Accuracy: 0.7952

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.6647        | 0.16  | 2000  | 2.1460          | 0.7356   |
| 2.2236        | 0.33  | 4000  | 1.9523          | 0.7603   |
| 2.0417        | 0.49  | 6000  | 1.7641          | 0.7668   |
| 1.9053        | 0.65  | 8000  | 1.5996          | 0.7717   |
| 1.807         | 0.81  | 10000 | 1.5363          | 0.7794   |
| 1.7398        | 0.98  | 12000 | 1.5253          | 0.7776   |
| 1.3197        | 1.14  | 14000 | 1.4476          | 0.7868   |
| 1.3269        | 1.3   | 16000 | 1.4379          | 0.7858   |
| 1.3669        | 1.47  | 18000 | 1.4432          | 0.7873   |
| 1.3291        | 1.63  | 20000 | 1.4059          | 0.7917   |
| 1.3257        | 1.79  | 22000 | 1.3745          | 0.7908   |
| 1.3308        | 1.96  | 24000 | 1.3805          | 0.7918   |
| 1.0251        | 2.12  | 26000 | 1.3870          | 0.7939   |
| 1.033         | 2.28  | 28000 | 1.3765          | 0.7974   |
| 1.0371        | 2.44  | 30000 | 1.3706          | 0.7963   |
| 1.061         | 2.61  | 32000 | 1.3873          | 0.7966   |
| 1.0679        | 2.77  | 34000 | 1.3142          | 0.8047   |
| 1.069         | 2.93  | 36000 | 1.3791          | 0.7900   |
| 0.8505        | 3.1   | 38000 | 1.3543          | 0.7990   |
| 0.8823        | 3.26  | 40000 | 1.2918          | 0.8029   |
| 0.9189        | 3.42  | 42000 | 1.3039          | 0.7972   |
| 0.9081        | 3.59  | 44000 | 1.3373          | 0.7952   |


### Framework versions

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