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phobert-base_v3

Text Classification
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roberta
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  1. README.md +88 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
  5. model_predict.csv +0 -0
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_v3
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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_v3
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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: 1.2798
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+ - Accuracy: 0.7805
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+ - Precision Macro: 0.7813
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+ - Recall Macro: 0.7807
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+ - F1 Macro: 0.7806
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+ - F1 Weighted: 0.7806
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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: 5e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 256
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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: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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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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+ | 1.0861 | 1.0 | 72 | 0.8945 | 0.5863 | 0.6024 | 0.5883 | 0.5734 | 0.5725 |
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+ | 0.8854 | 2.0 | 144 | 0.7401 | 0.6993 | 0.7354 | 0.6992 | 0.6982 | 0.6983 |
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+ | 0.5405 | 3.0 | 216 | 0.5891 | 0.7814 | 0.7817 | 0.7816 | 0.7813 | 0.7813 |
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+ | 0.4119 | 4.0 | 288 | 0.6523 | 0.7761 | 0.7776 | 0.7758 | 0.7760 | 0.7760 |
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+ | 0.2355 | 5.0 | 360 | 0.6712 | 0.7894 | 0.7899 | 0.7892 | 0.7894 | 0.7894 |
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+ | 0.1786 | 6.0 | 432 | 0.8116 | 0.7725 | 0.7733 | 0.7726 | 0.7726 | 0.7726 |
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+ | 0.1126 | 7.0 | 504 | 0.8907 | 0.7761 | 0.7792 | 0.7761 | 0.7761 | 0.7761 |
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+ | 0.0844 | 8.0 | 576 | 0.9184 | 0.7827 | 0.7834 | 0.7825 | 0.7826 | 0.7827 |
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+ | 0.0657 | 9.0 | 648 | 1.0276 | 0.7734 | 0.7769 | 0.7735 | 0.7737 | 0.7737 |
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+ | 0.0458 | 10.0 | 720 | 1.2265 | 0.7583 | 0.7713 | 0.7581 | 0.7582 | 0.7583 |
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+ | 0.0494 | 11.0 | 792 | 1.1001 | 0.7783 | 0.7793 | 0.7783 | 0.7784 | 0.7784 |
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+ | 0.0307 | 12.0 | 864 | 1.1487 | 0.7783 | 0.7798 | 0.7781 | 0.7783 | 0.7783 |
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+ | 0.0284 | 13.0 | 936 | 1.1877 | 0.7805 | 0.7812 | 0.7805 | 0.7805 | 0.7805 |
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+ | 0.0192 | 14.0 | 1008 | 1.2280 | 0.7836 | 0.7843 | 0.7839 | 0.7836 | 0.7836 |
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+ | 0.0172 | 15.0 | 1080 | 1.2466 | 0.7823 | 0.7823 | 0.7823 | 0.7823 | 0.7823 |
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+ | 0.0108 | 16.0 | 1152 | 1.2673 | 0.7809 | 0.7837 | 0.7810 | 0.7812 | 0.7812 |
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+ | 0.0111 | 17.0 | 1224 | 1.2614 | 0.7823 | 0.7825 | 0.7823 | 0.7823 | 0.7823 |
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+ | 0.0094 | 18.0 | 1296 | 1.2754 | 0.7814 | 0.7817 | 0.7815 | 0.7814 | 0.7815 |
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+ | 0.0079 | 19.0 | 1368 | 1.2823 | 0.7809 | 0.7821 | 0.7811 | 0.7811 | 0.7811 |
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+ | 0.0095 | 20.0 | 1440 | 1.2798 | 0.7805 | 0.7813 | 0.7807 | 0.7806 | 0.7806 |
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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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+ entailment 0.79 0.78 0.79 750
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+ contradiction 0.74 0.78 0.76 737
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+ neutral 0.79 0.76 0.78 777
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+
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+ accuracy 0.77 2264
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+ macro avg 0.77 0.77 0.77 2264
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+ weighted avg 0.77 0.77 0.77 2264
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+
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+ Confusion matrix:
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+ [[586 101 63]
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+ [ 75 572 90]
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+ [ 80 105 592]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,586,101,63
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+ contradiction,75,572,90
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+ neutral,80,105,592
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model_predict.csv ADDED
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