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phobert-base_v2

Text Classification
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roberta
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  1. README.md +87 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: vinai/phobert-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: phobert-base_v2
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+ results: []
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+ ---
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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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+
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+ # phobert-base_v2
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+
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+ This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3362
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+ - Accuracy: 0.9482
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+ - Precision Macro: 0.8854
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+ - Recall Macro: 0.8318
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+ - F1 Macro: 0.8543
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+ - F1 Weighted: 0.9464
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-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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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 128
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
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+ | 0.4592 | 1.0 | 90 | 0.2280 | 0.9356 | 0.8885 | 0.7440 | 0.7800 | 0.9283 |
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+ | 0.1801 | 2.0 | 180 | 0.1823 | 0.9476 | 0.8617 | 0.8443 | 0.8523 | 0.9469 |
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+ | 0.1221 | 3.0 | 270 | 0.1834 | 0.9482 | 0.8795 | 0.8359 | 0.8548 | 0.9467 |
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+ | 0.1071 | 4.0 | 360 | 0.1868 | 0.9520 | 0.9086 | 0.8096 | 0.8447 | 0.9486 |
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+ | 0.0817 | 5.0 | 450 | 0.2031 | 0.9526 | 0.8980 | 0.8393 | 0.8635 | 0.9508 |
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+ | 0.065 | 6.0 | 540 | 0.2240 | 0.9501 | 0.8908 | 0.8084 | 0.8389 | 0.9469 |
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+ | 0.0574 | 7.0 | 630 | 0.2219 | 0.9501 | 0.8625 | 0.8701 | 0.8662 | 0.9504 |
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+ | 0.0481 | 8.0 | 720 | 0.2503 | 0.9469 | 0.8752 | 0.8266 | 0.8472 | 0.9451 |
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+ | 0.0362 | 9.0 | 810 | 0.2489 | 0.9495 | 0.8822 | 0.8121 | 0.8392 | 0.9466 |
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+ | 0.0319 | 10.0 | 900 | 0.2584 | 0.9501 | 0.8784 | 0.8413 | 0.8577 | 0.9488 |
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+ | 0.0263 | 11.0 | 990 | 0.2774 | 0.9488 | 0.8800 | 0.8281 | 0.8498 | 0.9469 |
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+ | 0.0199 | 12.0 | 1080 | 0.2790 | 0.9501 | 0.8780 | 0.8416 | 0.8577 | 0.9488 |
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+ | 0.0114 | 13.0 | 1170 | 0.2955 | 0.9476 | 0.8733 | 0.8393 | 0.8546 | 0.9463 |
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+ | 0.0126 | 14.0 | 1260 | 0.3105 | 0.9501 | 0.8953 | 0.8331 | 0.8586 | 0.9481 |
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+ | 0.0125 | 15.0 | 1350 | 0.3147 | 0.9482 | 0.8773 | 0.8397 | 0.8564 | 0.9469 |
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+ | 0.0106 | 16.0 | 1440 | 0.3247 | 0.9469 | 0.8861 | 0.8350 | 0.8567 | 0.9453 |
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+ | 0.0065 | 17.0 | 1530 | 0.3419 | 0.9476 | 0.8751 | 0.8274 | 0.8476 | 0.9458 |
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+ | 0.0072 | 18.0 | 1620 | 0.3406 | 0.9469 | 0.8933 | 0.8185 | 0.8475 | 0.9444 |
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+ | 0.0058 | 19.0 | 1710 | 0.3389 | 0.9495 | 0.8904 | 0.8328 | 0.8566 | 0.9476 |
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+ | 0.0064 | 20.0 | 1800 | 0.3362 | 0.9482 | 0.8854 | 0.8318 | 0.8543 | 0.9464 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.0
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
classification_report_test.txt ADDED
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+ precision recall f1-score support
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+
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+ negative 0.96 0.96 0.96 1409
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+ neutral 0.63 0.60 0.62 167
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+ positive 0.95 0.96 0.96 1590
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+
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+ accuracy 0.94 3166
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+ macro avg 0.85 0.84 0.84 3166
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+ weighted avg 0.94 0.94 0.94 3166
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+
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+ Confusion matrix:
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+ [[1350 32 27]
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+ [ 21 101 45]
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+ [ 39 28 1523]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1350,32,27
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+ neutral,21,101,45
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+ positive,39,28,1523
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