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phobert-v2_v2

Text Classification
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roberta
Generated from Trainer
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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: agpl-3.0
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+ base_model: vinai/phobert-base-v2
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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-v2_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-v2_v2
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+
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+ This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3130
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+ - Accuracy: 0.9507
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+ - Precision Macro: 0.8904
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+ - Recall Macro: 0.8541
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+ - F1 Macro: 0.8704
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+ - F1 Weighted: 0.9497
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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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+ | 0.4636 | 1.0 | 90 | 0.2180 | 0.9419 | 0.9099 | 0.7652 | 0.8049 | 0.9359 |
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+ | 0.1882 | 2.0 | 180 | 0.1916 | 0.9419 | 0.8351 | 0.8649 | 0.8485 | 0.9433 |
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+ | 0.1453 | 3.0 | 270 | 0.1898 | 0.9488 | 0.8743 | 0.8402 | 0.8555 | 0.9476 |
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+ | 0.1175 | 4.0 | 360 | 0.1932 | 0.9526 | 0.9141 | 0.8267 | 0.8597 | 0.9500 |
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+ | 0.0856 | 5.0 | 450 | 0.2092 | 0.9514 | 0.8708 | 0.8711 | 0.8709 | 0.9514 |
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+ | 0.0826 | 6.0 | 540 | 0.2221 | 0.9526 | 0.9063 | 0.8516 | 0.8748 | 0.9512 |
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+ | 0.0675 | 7.0 | 630 | 0.2342 | 0.9438 | 0.8419 | 0.8696 | 0.8545 | 0.9450 |
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+ | 0.0618 | 8.0 | 720 | 0.2402 | 0.9469 | 0.8890 | 0.8430 | 0.8630 | 0.9456 |
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+ | 0.0426 | 9.0 | 810 | 0.2503 | 0.9507 | 0.8797 | 0.8543 | 0.8660 | 0.9499 |
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+ | 0.038 | 10.0 | 900 | 0.2786 | 0.9514 | 0.8999 | 0.8467 | 0.8692 | 0.9499 |
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+ | 0.039 | 11.0 | 990 | 0.2795 | 0.9463 | 0.8628 | 0.8554 | 0.8589 | 0.9460 |
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+ | 0.0263 | 12.0 | 1080 | 0.2817 | 0.9488 | 0.8733 | 0.8571 | 0.8648 | 0.9483 |
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+ | 0.0209 | 13.0 | 1170 | 0.2840 | 0.9495 | 0.8802 | 0.8576 | 0.8681 | 0.9488 |
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+ | 0.0221 | 14.0 | 1260 | 0.2769 | 0.9526 | 0.8904 | 0.8639 | 0.8761 | 0.9519 |
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+ | 0.0172 | 15.0 | 1350 | 0.2861 | 0.9514 | 0.8985 | 0.8546 | 0.8739 | 0.9502 |
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+ | 0.0159 | 16.0 | 1440 | 0.3031 | 0.9482 | 0.8850 | 0.8523 | 0.8671 | 0.9472 |
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+ | 0.0124 | 17.0 | 1530 | 0.3119 | 0.9501 | 0.8792 | 0.8538 | 0.8655 | 0.9493 |
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+ | 0.0107 | 18.0 | 1620 | 0.3173 | 0.9476 | 0.8948 | 0.8476 | 0.8681 | 0.9463 |
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+ | 0.0106 | 19.0 | 1710 | 0.3094 | 0.9545 | 0.8979 | 0.8611 | 0.8776 | 0.9535 |
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+ | 0.0108 | 20.0 | 1800 | 0.3130 | 0.9507 | 0.8904 | 0.8541 | 0.8704 | 0.9497 |
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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 0.94 0.96 0.95 1409
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+ neutral 0.70 0.50 0.58 167
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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 0.86 0.81 0.83 3166
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+ weighted avg 0.93 0.94 0.94 3166
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+
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+ Confusion matrix:
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+ [[1359 21 29]
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+ [ 34 83 50]
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+ [ 47 15 1528]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1359,21,29
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+ neutral,34,83,50
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+ positive,47,15,1528
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