---
base_model: Fsoft-AIC/videberta-xsmall
tags:
- generated_from_trainer
datasets:
- vietnamese_students_feedback
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: videberta-sentiment-analysis
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: vietnamese_students_feedback
      type: vietnamese_students_feedback
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9496688741721855
    - name: Precision
      type: precision
      value: 0.9539227895392279
    - name: Recall
      type: recall
      value: 0.9515527950310559
    - name: F1
      type: f1
      value: 0.9527363184079602
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# videberta-sentiment-analysis

This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on the vietnamese_students_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2903
- Accuracy: 0.9497
- Precision: 0.9539
- Recall: 0.9516
- F1: 0.9527

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2029        | 2.91  | 500  | 0.2022          | 0.9358   | 0.9414    | 0.9379 | 0.9396 |
| 0.1435        | 5.81  | 1000 | 0.2109          | 0.9325   | 0.9200    | 0.9565 | 0.9379 |
| 0.1023        | 8.72  | 1500 | 0.2648          | 0.9344   | 0.9263    | 0.9528 | 0.9394 |
| 0.08          | 11.63 | 2000 | 0.2360          | 0.9437   | 0.9455    | 0.9491 | 0.9473 |
| 0.0628        | 14.53 | 2500 | 0.2758          | 0.9417   | 0.9377    | 0.9540 | 0.9458 |
| 0.0493        | 17.44 | 3000 | 0.3189          | 0.9351   | 0.9223    | 0.9590 | 0.9403 |
| 0.0397        | 20.35 | 3500 | 0.3662          | 0.9377   | 0.9257    | 0.9602 | 0.9427 |
| 0.0318        | 23.26 | 4000 | 0.2903          | 0.9497   | 0.9539    | 0.9516 | 0.9527 |
| 0.0244        | 26.16 | 4500 | 0.3962          | 0.9450   | 0.9381    | 0.9602 | 0.9490 |
| 0.0176        | 29.07 | 5000 | 0.3940          | 0.9464   | 0.9425    | 0.9578 | 0.9501 |
| 0.0165        | 31.98 | 5500 | 0.3990          | 0.9411   | 0.9486    | 0.9404 | 0.9445 |
| 0.0139        | 34.88 | 6000 | 0.4565          | 0.9424   | 0.9336    | 0.9602 | 0.9467 |
| 0.0123        | 37.79 | 6500 | 0.3779          | 0.9457   | 0.9491    | 0.9491 | 0.9491 |
| 0.0118        | 40.7  | 7000 | 0.4308          | 0.9444   | 0.9380    | 0.9590 | 0.9484 |
| 0.0086        | 43.6  | 7500 | 0.4732          | 0.9404   | 0.9344    | 0.9553 | 0.9447 |
| 0.0076        | 46.51 | 8000 | 0.4197          | 0.9457   | 0.9547    | 0.9429 | 0.9487 |
| 0.0067        | 49.42 | 8500 | 0.4952          | 0.9444   | 0.9391    | 0.9578 | 0.9483 |
| 0.0062        | 52.33 | 9000 | 0.4907          | 0.9437   | 0.9444    | 0.9503 | 0.9474 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3