---
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: 0.2129
- Accuracy: 0.94
- F1: 0.9394

## 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        | 0.01  | 50   | 0.6913          | 0.55     | 0.3903 |
| No log        | 0.02  | 100  | 0.6909          | 0.59     | 0.5186 |
| No log        | 0.03  | 150  | 0.6934          | 0.45     | 0.2793 |
| No log        | 0.04  | 200  | 0.6889          | 0.57     | 0.5709 |
| No log        | 0.05  | 250  | 0.6818          | 0.56     | 0.5607 |
| No log        | 0.06  | 300  | 0.6854          | 0.56     | 0.5607 |
| No log        | 0.07  | 350  | 0.6878          | 0.56     | 0.5607 |
| No log        | 0.08  | 400  | 0.7014          | 0.56     | 0.5607 |
| No log        | 0.09  | 450  | 0.6797          | 0.56     | 0.5607 |
| 0.6799        | 0.1   | 500  | 0.6731          | 0.56     | 0.5607 |
| 0.6799        | 0.11  | 550  | 0.6490          | 0.64     | 0.6203 |
| 0.6799        | 0.12  | 600  | 0.6456          | 0.71     | 0.7049 |
| 0.6799        | 0.13  | 650  | 0.6259          | 0.64     | 0.6203 |
| 0.6799        | 0.14  | 700  | 0.5264          | 0.83     | 0.8304 |
| 0.6799        | 0.15  | 750  | 0.4671          | 0.83     | 0.8304 |
| 0.6799        | 0.16  | 800  | 0.3387          | 0.94     | 0.9394 |
| 0.6799        | 0.17  | 850  | 0.2935          | 0.94     | 0.9394 |
| 0.6799        | 0.18  | 900  | 0.2604          | 0.94     | 0.9394 |
| 0.6799        | 0.19  | 950  | 0.2443          | 0.94     | 0.9394 |
| 0.4884        | 0.2   | 1000 | 0.2355          | 0.94     | 0.9394 |
| 0.4884        | 0.2   | 1050 | 0.2286          | 0.94     | 0.9394 |
| 0.4884        | 0.21  | 1100 | 0.2240          | 0.94     | 0.9394 |
| 0.4884        | 0.22  | 1150 | 0.2201          | 0.94     | 0.9394 |
| 0.4884        | 0.23  | 1200 | 0.2165          | 0.94     | 0.9394 |
| 0.4884        | 0.24  | 1250 | 0.2129          | 0.94     | 0.9394 |


### Framework versions

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