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--- |
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base_model: Fsoft-AIC/videberta-xsmall |
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tags: |
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- generated_from_trainer |
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datasets: |
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- vietnamese_students_feedback |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: videberta-sentiment-analysis |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: vietnamese_students_feedback |
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type: vietnamese_students_feedback |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9496688741721855 |
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- name: Precision |
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type: precision |
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value: 0.9539227895392279 |
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- name: Recall |
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type: recall |
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value: 0.9515527950310559 |
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- name: F1 |
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type: f1 |
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value: 0.9527363184079602 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# videberta-sentiment-analysis |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2903 |
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- Accuracy: 0.9497 |
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- Precision: 0.9539 |
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- Recall: 0.9516 |
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- F1: 0.9527 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.2029 | 2.91 | 500 | 0.2022 | 0.9358 | 0.9414 | 0.9379 | 0.9396 | |
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| 0.1435 | 5.81 | 1000 | 0.2109 | 0.9325 | 0.9200 | 0.9565 | 0.9379 | |
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| 0.1023 | 8.72 | 1500 | 0.2648 | 0.9344 | 0.9263 | 0.9528 | 0.9394 | |
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| 0.08 | 11.63 | 2000 | 0.2360 | 0.9437 | 0.9455 | 0.9491 | 0.9473 | |
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| 0.0628 | 14.53 | 2500 | 0.2758 | 0.9417 | 0.9377 | 0.9540 | 0.9458 | |
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| 0.0493 | 17.44 | 3000 | 0.3189 | 0.9351 | 0.9223 | 0.9590 | 0.9403 | |
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| 0.0397 | 20.35 | 3500 | 0.3662 | 0.9377 | 0.9257 | 0.9602 | 0.9427 | |
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| 0.0318 | 23.26 | 4000 | 0.2903 | 0.9497 | 0.9539 | 0.9516 | 0.9527 | |
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| 0.0244 | 26.16 | 4500 | 0.3962 | 0.9450 | 0.9381 | 0.9602 | 0.9490 | |
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| 0.0176 | 29.07 | 5000 | 0.3940 | 0.9464 | 0.9425 | 0.9578 | 0.9501 | |
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| 0.0165 | 31.98 | 5500 | 0.3990 | 0.9411 | 0.9486 | 0.9404 | 0.9445 | |
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| 0.0139 | 34.88 | 6000 | 0.4565 | 0.9424 | 0.9336 | 0.9602 | 0.9467 | |
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| 0.0123 | 37.79 | 6500 | 0.3779 | 0.9457 | 0.9491 | 0.9491 | 0.9491 | |
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| 0.0118 | 40.7 | 7000 | 0.4308 | 0.9444 | 0.9380 | 0.9590 | 0.9484 | |
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| 0.0086 | 43.6 | 7500 | 0.4732 | 0.9404 | 0.9344 | 0.9553 | 0.9447 | |
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| 0.0076 | 46.51 | 8000 | 0.4197 | 0.9457 | 0.9547 | 0.9429 | 0.9487 | |
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| 0.0067 | 49.42 | 8500 | 0.4952 | 0.9444 | 0.9391 | 0.9578 | 0.9483 | |
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| 0.0062 | 52.33 | 9000 | 0.4907 | 0.9437 | 0.9444 | 0.9503 | 0.9474 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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