---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NLPGroupProject-Finetune-Bert
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# NLPGroupProject-Finetune-Bert

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2570
- Accuracy: 0.716

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.25  | 250  | 0.9015          | 0.677    |
| 0.9879        | 0.5   | 500  | 0.8100          | 0.71     |
| 0.9879        | 0.75  | 750  | 0.9159          | 0.709    |
| 0.8313        | 1.0   | 1000 | 0.9674          | 0.722    |
| 0.8313        | 1.25  | 1250 | 0.9637          | 0.719    |
| 0.628         | 1.5   | 1500 | 0.7818          | 0.719    |
| 0.628         | 1.75  | 1750 | 0.9127          | 0.721    |
| 0.6537        | 2.0   | 2000 | 0.8752          | 0.722    |
| 0.6537        | 2.25  | 2250 | 1.3051          | 0.716    |
| 0.4037        | 2.5   | 2500 | 1.2484          | 0.712    |
| 0.4037        | 2.75  | 2750 | 1.2599          | 0.72     |
| 0.3853        | 3.0   | 3000 | 1.2570          | 0.716    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1