---
license: mit
base_model: prajjwal1/bert-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-small-finetuned
  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. -->

# bert-small-finetuned

This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9941
- Accuracy: 0.5903
- F1 Score: 0.5865

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log        | 1.0   | 24   | 1.2145          | 0.4933   | 0.4701   |
| No log        | 2.0   | 48   | 1.0960          | 0.5391   | 0.5365   |
| No log        | 3.0   | 72   | 1.0569          | 0.5768   | 0.5791   |
| No log        | 4.0   | 96   | 1.0052          | 0.5714   | 0.5698   |
| No log        | 5.0   | 120  | 0.9889          | 0.5714   | 0.5702   |
| No log        | 6.0   | 144  | 0.9932          | 0.5795   | 0.5772   |
| No log        | 7.0   | 168  | 0.9841          | 0.5714   | 0.5680   |
| No log        | 8.0   | 192  | 0.9941          | 0.5903   | 0.5865   |
| No log        | 9.0   | 216  | 0.9788          | 0.5903   | 0.5891   |
| No log        | 10.0  | 240  | 1.0105          | 0.5660   | 0.5617   |
| No log        | 11.0  | 264  | 1.0473          | 0.5526   | 0.5464   |
| No log        | 12.0  | 288  | 1.0272          | 0.5714   | 0.5685   |
| No log        | 13.0  | 312  | 1.0627          | 0.5499   | 0.5492   |
| No log        | 14.0  | 336  | 1.0428          | 0.5795   | 0.5782   |
| No log        | 15.0  | 360  | 1.0644          | 0.5633   | 0.5625   |
| No log        | 16.0  | 384  | 1.1463          | 0.5364   | 0.5261   |
| No log        | 17.0  | 408  | 1.1109          | 0.5714   | 0.5689   |
| No log        | 18.0  | 432  | 1.1260          | 0.5741   | 0.5739   |
| No log        | 19.0  | 456  | 1.1793          | 0.5580   | 0.5533   |
| No log        | 20.0  | 480  | 1.1968          | 0.5580   | 0.5535   |
| 0.6103        | 21.0  | 504  | 1.1961          | 0.5741   | 0.5722   |
| 0.6103        | 22.0  | 528  | 1.2399          | 0.5553   | 0.5504   |
| 0.6103        | 23.0  | 552  | 1.2642          | 0.5526   | 0.5473   |
| 0.6103        | 24.0  | 576  | 1.2530          | 0.5660   | 0.5625   |
| 0.6103        | 25.0  | 600  | 1.2637          | 0.5714   | 0.5687   |
| 0.6103        | 26.0  | 624  | 1.3012          | 0.5526   | 0.5468   |
| 0.6103        | 27.0  | 648  | 1.2932          | 0.5606   | 0.5579   |
| 0.6103        | 28.0  | 672  | 1.2888          | 0.5687   | 0.5664   |
| 0.6103        | 29.0  | 696  | 1.3087          | 0.5660   | 0.5634   |
| 0.6103        | 30.0  | 720  | 1.3073          | 0.5714   | 0.5687   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1