---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm_r_large-baseline_model-v2-fallen-oath-3
  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. -->

# xlm_r_large-baseline_model-v2-fallen-oath-3

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on [SOLD](https://huggingface.co/datasets/sinhala-nlp/SOLD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5036
- Precision 0: 0.8766
- Precision 1: 0.7911
- Recall 0: 0.8512
- Recall 1: 0.8246
- F1 0: 0.8637
- F1 1: 0.8075
- Precision Weighted: 0.8419
- Recall Weighted: 0.8404
- F1 Weighted: 0.8409
- F1 Macro: 0.8356

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision 0 | Precision 1 | Recall 0 | Recall 1 | F1 0   | F1 1   | Precision Weighted | Recall Weighted | F1 Weighted | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-----------:|:--------:|:--------:|:------:|:------:|:------------------:|:---------------:|:-----------:|:--------:|
| 0.4937        | 1.0   | 469  | 0.4268          | 0.8346      | 0.7933      | 0.8667   | 0.7488   | 0.8503 | 0.7704 | 0.8179             | 0.8188          | 0.8179      | 0.8104   |
| 0.3945        | 2.0   | 938  | 0.3987          | 0.9083      | 0.7168      | 0.7603   | 0.8877   | 0.8277 | 0.7931 | 0.8305             | 0.812           | 0.8137      | 0.8104   |
| 0.3721        | 3.0   | 1407 | 0.3612          | 0.8654      | 0.7992      | 0.8620   | 0.8039   | 0.8637 | 0.8016 | 0.8386             | 0.8384          | 0.8385      | 0.8326   |
| 0.2721        | 4.0   | 1876 | 0.4191          | 0.8514      | 0.8246      | 0.8875   | 0.7734   | 0.8691 | 0.7982 | 0.8405             | 0.8412          | 0.8403      | 0.8336   |
| 0.2144        | 5.0   | 2345 | 0.5036          | 0.8766      | 0.7911      | 0.8512   | 0.8246   | 0.8637 | 0.8075 | 0.8419             | 0.8404          | 0.8409      | 0.8356   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1