---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: MRC_ER_videberta-base_word_ViWikiFC
  results: []
---

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# MRC_ER_videberta-base_word_ViWikiFC

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: 2.0166
- Exact Match: 0.7804
- F1: 0.8093

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:|
| 0.6374        | 1.0   | 2093  | 1.7652          | 0.7622      | 0.7962 |
| 0.5612        | 2.0   | 4186  | 1.7521          | 0.7718      | 0.8046 |
| 0.5021        | 3.0   | 6279  | 1.8670          | 0.7823      | 0.8110 |
| 0.4184        | 4.0   | 8372  | 1.9411          | 0.7823      | 0.8087 |
| 0.381         | 5.0   | 10465 | 2.0166          | 0.7804      | 0.8093 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2