---
library_name: transformers
license: mit
base_model: SemViQA/tc-xlmr-viwikifc
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tc-xlmr-viwikifc-finetuned-vihallu-fold-3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tc-xlmr-viwikifc-finetuned-vihallu-fold-3

This model is a fine-tuned version of [SemViQA/tc-xlmr-viwikifc](https://huggingface.co/SemViQA/tc-xlmr-viwikifc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5775
- Accuracy: 0.8114
- F1 Macro: 0.8120

## 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.6577        | 1.0   | 350  | 0.5663          | 0.7879   | 0.7876   |
| 0.4734        | 2.0   | 700  | 0.5775          | 0.8114   | 0.8120   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.8.0+cu128
- Datasets 2.20.0
- Tokenizers 0.19.1