---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: TestForColab
  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. -->

# TestForColab

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6515
- Accuracy: 0.56
- F1: 0.5579

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.0   | 50   | 0.6897          | 0.54     | 0.3787 |
| No log        | 0.01  | 100  | 0.6899          | 0.6      | 0.5926 |
| No log        | 0.01  | 150  | 0.6952          | 0.46     | 0.2899 |
| No log        | 0.01  | 200  | 0.6874          | 0.63     | 0.6194 |
| No log        | 0.02  | 250  | 0.6849          | 0.64     | 0.6092 |
| No log        | 0.02  | 300  | 0.6929          | 0.46     | 0.2899 |
| No log        | 0.02  | 350  | 0.6830          | 0.6      | 0.5390 |
| No log        | 0.03  | 400  | 0.6821          | 0.54     | 0.3787 |
| No log        | 0.03  | 450  | 0.6812          | 0.63     | 0.6095 |
| 0.6924        | 0.03  | 500  | 0.6806          | 0.62     | 0.6077 |
| 0.6924        | 0.04  | 550  | 0.6770          | 0.62     | 0.5969 |
| 0.6924        | 0.04  | 600  | 0.6805          | 0.58     | 0.5746 |
| 0.6924        | 0.04  | 650  | 0.6800          | 0.59     | 0.5857 |
| 0.6924        | 0.05  | 700  | 0.6732          | 0.63     | 0.6008 |
| 0.6924        | 0.05  | 750  | 0.6820          | 0.56     | 0.5387 |
| 0.6924        | 0.05  | 800  | 0.6652          | 0.64     | 0.6253 |
| 0.6924        | 0.06  | 850  | 0.6634          | 0.59     | 0.5896 |
| 0.6924        | 0.06  | 900  | 0.6604          | 0.61     | 0.6103 |
| 0.6924        | 0.06  | 950  | 0.6733          | 0.62     | 0.5936 |
| 0.6842        | 0.07  | 1000 | 0.6590          | 0.65     | 0.6176 |
| 0.6842        | 0.07  | 1050 | 0.6549          | 0.6      | 0.6005 |
| 0.6842        | 0.07  | 1100 | 0.6521          | 0.63     | 0.6242 |
| 0.6842        | 0.08  | 1150 | 0.6524          | 0.61     | 0.6015 |
| 0.6842        | 0.08  | 1200 | 0.6587          | 0.57     | 0.5634 |
| 0.6842        | 0.09  | 1250 | 0.6515          | 0.56     | 0.5579 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0