---
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: 0.9470198675496688
    - name: Precision
      type: precision
      value: 0.9480840543881335
    - name: Recall
      type: recall
      value: 0.9527950310559006
    - name: F1
      type: f1
      value: 0.9504337050805451
---

<!-- 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. -->

# 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: 0.2787
- Accuracy: 0.9470
- Precision: 0.9481
- Recall: 0.9528
- F1: 0.9504

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6152        | 0.58  | 100  | 0.4777          | 0.8007   | 0.8580    | 0.7503 | 0.8005 |
| 0.408         | 1.16  | 200  | 0.3241          | 0.8669   | 0.8943    | 0.8509 | 0.8721 |
| 0.3268        | 1.74  | 300  | 0.2726          | 0.8954   | 0.8837    | 0.9255 | 0.9041 |
| 0.2654        | 2.33  | 400  | 0.2296          | 0.9199   | 0.9212    | 0.9292 | 0.9252 |
| 0.253         | 2.91  | 500  | 0.2088          | 0.9159   | 0.9206    | 0.9217 | 0.9212 |
| 0.2014        | 3.49  | 600  | 0.2318          | 0.9172   | 0.9028    | 0.9466 | 0.9242 |
| 0.1939        | 4.07  | 700  | 0.2131          | 0.9212   | 0.9224    | 0.9304 | 0.9264 |
| 0.1698        | 4.65  | 800  | 0.2005          | 0.9311   | 0.9499    | 0.9193 | 0.9343 |
| 0.1822        | 5.23  | 900  | 0.2249          | 0.9245   | 0.9089    | 0.9540 | 0.9309 |
| 0.1441        | 5.81  | 1000 | 0.2038          | 0.9311   | 0.9311    | 0.9404 | 0.9357 |
| 0.1403        | 6.4   | 1100 | 0.2044          | 0.9338   | 0.9315    | 0.9453 | 0.9383 |
| 0.1377        | 6.98  | 1200 | 0.1991          | 0.9417   | 0.9567    | 0.9329 | 0.9447 |
| 0.1191        | 7.56  | 1300 | 0.2955          | 0.9119   | 0.8792    | 0.9677 | 0.9213 |
| 0.1227        | 8.14  | 1400 | 0.2362          | 0.9318   | 0.9199    | 0.9553 | 0.9372 |
| 0.1023        | 8.72  | 1500 | 0.2221          | 0.9358   | 0.9286    | 0.9528 | 0.9405 |
| 0.1049        | 9.3   | 1600 | 0.1940          | 0.9424   | 0.9454    | 0.9466 | 0.9460 |
| 0.1002        | 9.88  | 1700 | 0.1949          | 0.9404   | 0.9649    | 0.9217 | 0.9428 |
| 0.0946        | 10.47 | 1800 | 0.2232          | 0.9404   | 0.9625    | 0.9242 | 0.9430 |
| 0.0911        | 11.05 | 1900 | 0.2016          | 0.9457   | 0.9641    | 0.9329 | 0.9482 |
| 0.0818        | 11.63 | 2000 | 0.2636          | 0.9311   | 0.9128    | 0.9627 | 0.9371 |
| 0.0889        | 12.21 | 2100 | 0.2279          | 0.9450   | 0.9524    | 0.9441 | 0.9482 |
| 0.0668        | 12.79 | 2200 | 0.2460          | 0.9411   | 0.9409    | 0.9491 | 0.9450 |
| 0.0635        | 13.37 | 2300 | 0.2764          | 0.9424   | 0.9465    | 0.9453 | 0.9459 |
| 0.072         | 13.95 | 2400 | 0.2519          | 0.9437   | 0.9390    | 0.9565 | 0.9477 |
| 0.0697        | 14.53 | 2500 | 0.2705          | 0.9404   | 0.9408    | 0.9478 | 0.9443 |
| 0.0602        | 15.12 | 2600 | 0.2686          | 0.9450   | 0.9513    | 0.9453 | 0.9483 |
| 0.065         | 15.7  | 2700 | 0.2629          | 0.9450   | 0.9501    | 0.9466 | 0.9484 |
| 0.0628        | 16.28 | 2800 | 0.2644          | 0.9450   | 0.9547    | 0.9416 | 0.9481 |
| 0.0505        | 16.86 | 2900 | 0.2704          | 0.9424   | 0.9400    | 0.9528 | 0.9463 |
| 0.0471        | 17.44 | 3000 | 0.2787          | 0.9470   | 0.9481    | 0.9528 | 0.9504 |
| 0.0568        | 18.02 | 3100 | 0.2766          | 0.9450   | 0.9424    | 0.9553 | 0.9488 |
| 0.0523        | 18.6  | 3200 | 0.2659          | 0.9424   | 0.9421    | 0.9503 | 0.9462 |
| 0.0487        | 19.19 | 3300 | 0.3091          | 0.9338   | 0.9222    | 0.9565 | 0.9390 |
| 0.0529        | 19.77 | 3400 | 0.3575          | 0.9272   | 0.9045    | 0.9652 | 0.9339 |
| 0.0484        | 20.35 | 3500 | 0.3228          | 0.9358   | 0.9214    | 0.9615 | 0.9410 |
| 0.0456        | 20.93 | 3600 | 0.2694          | 0.9437   | 0.9412    | 0.9540 | 0.9476 |
| 0.0424        | 21.51 | 3700 | 0.2793          | 0.9404   | 0.9376    | 0.9516 | 0.9445 |
| 0.045         | 22.09 | 3800 | 0.2953          | 0.9417   | 0.9356    | 0.9565 | 0.9459 |
| 0.0395        | 22.67 | 3900 | 0.2840          | 0.9417   | 0.9377    | 0.9540 | 0.9458 |
| 0.0418        | 23.26 | 4000 | 0.3527          | 0.9305   | 0.9108    | 0.9640 | 0.9366 |


### Framework versions

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