---
license: mit
base_model: Ransaka/sinhala-bert-medium-v2
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: SentimentClassifier.si
  results: []
language:
- si
pipeline_tag: text-classification
---

<!-- 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. -->

# SentimentClassifier.si

This model is a fine-tuned version of [Ransaka/sinhala-bert-medium-v2](https://huggingface.co/Ransaka/sinhala-bert-medium-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2358
- F1: 0.8877

## Intended uses & limitations

More information needed

## Training and evaluation data

Labels
```plaintext
  NEGATIVE: 1
  POSITIVE: 0
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4053        | 0.08  | 100  | 0.2802          | 0.8677 |
| 0.3768        | 0.16  | 200  | 0.3123          | 0.8616 |
| 0.3334        | 0.24  | 300  | 0.2810          | 0.8732 |
| 0.2906        | 0.32  | 400  | 0.2554          | 0.8779 |
| 0.3027        | 0.4   | 500  | 0.2595          | 0.8836 |
| 0.2612        | 0.48  | 600  | 0.2797          | 0.8592 |
| 0.2568        | 0.56  | 700  | 0.2474          | 0.8785 |
| 0.2325        | 0.64  | 800  | 0.2546          | 0.8816 |
| 0.2272        | 0.72  | 900  | 0.2424          | 0.8878 |
| 0.2331        | 0.8   | 1000 | 0.2358          | 0.8877 |

Model performance on validation dataset

```plaintext
              precision    recall  f1-score   support

           0       0.95      0.92      0.93      6943
           1       0.82      0.88      0.84      2913

    accuracy                           0.90      9856
   macro avg       0.88      0.90      0.89      9856
weighted avg       0.91      0.90      0.91      9856
```

<img 
  src="https://cdn-uploads.huggingface.co/production/uploads/60f2e10dadf471cbdf8bb661/Yi9TbdOF6CoMfKk40Bcvu.png" 
  alt="Confusion Matrix on Validation Data" 
  width="300">

### Framework versions

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