| --- |
| 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 |
|
|
