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---
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 |