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-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-qqp-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: 1.2091
- Accuracy: 0.8917

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.9032        | 0.18  | 2000  | 2.5245          | 0.8335   |
| 2.4449        | 0.35  | 4000  | 1.9112          | 0.8606   |
| 2.2734        | 0.53  | 6000  | 1.7973          | 0.8612   |
| 2.1432        | 0.7   | 8000  | 1.6004          | 0.8726   |
| 2.0468        | 0.88  | 10000 | 1.5091          | 0.8770   |
| 1.4797        | 1.06  | 12000 | 1.5721          | 0.8797   |
| 1.437         | 1.23  | 14000 | 1.4794          | 0.8797   |
| 1.4702        | 1.41  | 16000 | 1.6706          | 0.8784   |
| 1.4579        | 1.58  | 18000 | 1.4041          | 0.8773   |
| 1.4283        | 1.76  | 20000 | 1.4245          | 0.8833   |
| 1.3841        | 1.93  | 22000 | 1.3917          | 0.8783   |
| 0.987         | 2.11  | 24000 | 1.2889          | 0.8864   |
| 0.9901        | 2.29  | 26000 | 1.3097          | 0.8841   |
| 1.0344        | 2.46  | 28000 | 1.3500          | 0.8883   |
| 1.0517        | 2.64  | 30000 | 1.3403          | 0.8877   |
| 1.0764        | 2.81  | 32000 | 1.3359          | 0.8881   |
| 1.1362        | 2.99  | 34000 | 1.3977          | 0.8860   |
| 0.7819        | 3.17  | 36000 | 1.3188          | 0.8857   |
| 0.8156        | 3.34  | 38000 | 1.3015          | 0.8904   |
| 0.8501        | 3.52  | 40000 | 1.4377          | 0.8903   |
| 0.8314        | 3.69  | 42000 | 1.3927          | 0.8909   |
| 0.8534        | 3.87  | 44000 | 1.2927          | 0.8917   |
| 0.6853        | 4.05  | 46000 | 1.3488          | 0.8913   |
| 0.6741        | 4.22  | 48000 | 1.3379          | 0.8914   |
| 0.7012        | 4.4   | 50000 | 1.2788          | 0.8955   |
| 0.7182        | 4.57  | 52000 | 1.3726          | 0.8942   |
| 0.7195        | 4.75  | 54000 | 1.3061          | 0.8951   |
| 0.739         | 4.93  | 56000 | 1.2797          | 0.8936   |
| 0.6055        | 5.1   | 58000 | 1.3500          | 0.8942   |
| 0.612         | 5.28  | 60000 | 1.2091          | 0.8917   |


### Framework versions

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