---
license: mit
base_model: SemViQA/tc-xlmr-viwikifc
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tc-xlmr-viwikifc-finetuned-vihallu-fold-1
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/spirit-vilm/dsc2025-vihallu-semviqa-finetuning/runs/dvc8p7ar)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/spirit-vilm/dsc2025-vihallu-semviqa-finetuning/runs/g3c8fduy)
# tc-xlmr-viwikifc-finetuned-vihallu-fold-1

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: 0.6098
- Accuracy: 0.7953
- F1 Macro: 0.7961

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 0.636         | 0.9982 | 412  | 0.6509          | 0.7650   | 0.7661   |
| 0.449         | 1.9964 | 824  | 0.6098          | 0.7953   | 0.7961   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1