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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: 0.9496688741721855
- 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
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: 0.2903
- 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
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