---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: mrc-videnerta-dsc
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mrc-videnerta-dsc

This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8100
- Exact Match: 0.5970
- F1: 0.7653

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Exact Match | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:|
| 0.967         | 1.0   | 3098  | 0.8918          | 0.5358      | 0.7128 |
| 0.8396        | 2.0   | 6196  | 0.8346          | 0.5610      | 0.7488 |
| 0.7209        | 3.0   | 9294  | 0.8057          | 0.5825      | 0.7624 |
| 0.6532        | 4.0   | 12392 | 0.8212          | 0.5951      | 0.7513 |
| 0.601         | 5.0   | 15490 | 0.8100          | 0.5970      | 0.7653 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1