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phobert-large_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-large
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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-large_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-large_v2
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+
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+ This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3603
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+ - Accuracy: 0.9520
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+ - Precision Macro: 0.8966
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+ - Recall Macro: 0.8344
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+ - F1 Macro: 0.8599
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+ - F1 Weighted: 0.9500
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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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+ | 0.4609 | 1.0 | 90 | 0.2266 | 0.9387 | 0.9153 | 0.7341 | 0.7705 | 0.9298 |
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+ | 0.1892 | 2.0 | 180 | 0.2063 | 0.9419 | 0.8750 | 0.8032 | 0.8303 | 0.9390 |
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+ | 0.1302 | 3.0 | 270 | 0.1810 | 0.9551 | 0.8937 | 0.8696 | 0.8809 | 0.9545 |
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+ | 0.0972 | 4.0 | 360 | 0.1825 | 0.9533 | 0.9077 | 0.8396 | 0.8671 | 0.9513 |
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+ | 0.0676 | 5.0 | 450 | 0.2135 | 0.9514 | 0.8737 | 0.8546 | 0.8636 | 0.9507 |
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+ | 0.0567 | 6.0 | 540 | 0.2387 | 0.9507 | 0.9005 | 0.8334 | 0.8605 | 0.9487 |
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+ | 0.047 | 7.0 | 630 | 0.2487 | 0.9419 | 0.8520 | 0.8401 | 0.8457 | 0.9414 |
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+ | 0.0405 | 8.0 | 720 | 0.3009 | 0.9501 | 0.9101 | 0.8004 | 0.8370 | 0.9462 |
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+ | 0.0225 | 9.0 | 810 | 0.2780 | 0.9514 | 0.8963 | 0.8217 | 0.8506 | 0.9488 |
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+ | 0.0275 | 10.0 | 900 | 0.2952 | 0.9514 | 0.8945 | 0.8421 | 0.8643 | 0.9498 |
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+ | 0.0141 | 11.0 | 990 | 0.3188 | 0.9488 | 0.8690 | 0.8611 | 0.8649 | 0.9486 |
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+ | 0.0122 | 12.0 | 1080 | 0.3221 | 0.9520 | 0.8983 | 0.8261 | 0.8545 | 0.9496 |
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+ | 0.0091 | 13.0 | 1170 | 0.3291 | 0.9526 | 0.9042 | 0.8430 | 0.8684 | 0.9509 |
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+ | 0.0053 | 14.0 | 1260 | 0.3365 | 0.9476 | 0.8795 | 0.8270 | 0.8490 | 0.9456 |
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+ | 0.0065 | 15.0 | 1350 | 0.3530 | 0.9520 | 0.9009 | 0.8345 | 0.8613 | 0.9500 |
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+ | 0.0038 | 16.0 | 1440 | 0.3478 | 0.9520 | 0.9033 | 0.8261 | 0.8561 | 0.9495 |
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+ | 0.0031 | 17.0 | 1530 | 0.3586 | 0.9501 | 0.9001 | 0.8329 | 0.8601 | 0.9481 |
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+ | 0.0016 | 18.0 | 1620 | 0.3596 | 0.9514 | 0.8947 | 0.8421 | 0.8645 | 0.9498 |
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+ | 0.0015 | 19.0 | 1710 | 0.3594 | 0.9520 | 0.8966 | 0.8344 | 0.8599 | 0.9500 |
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+ | 0.0014 | 20.0 | 1800 | 0.3603 | 0.9520 | 0.8966 | 0.8344 | 0.8599 | 0.9500 |
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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 0.95 0.95 0.95 1409
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+ neutral 0.59 0.62 0.60 167
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+ positive 0.95 0.96 0.96 1590
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+
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+ accuracy 0.93 3166
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+ macro avg 0.83 0.84 0.84 3166
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+ weighted avg 0.94 0.93 0.93 3166
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+
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+ Confusion matrix:
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+ [[1336 44 29]
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+ [ 21 103 43]
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+ [ 42 29 1519]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1336,44,29
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+ neutral,21,103,43
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+ positive,42,29,1519
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