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---
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
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