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phobert-base-v2_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: 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-base-v2_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-v2_v3
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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: 1.1699
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+ - Accuracy: 0.7991
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+ - Precision Macro: 0.7995
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+ - Recall Macro: 0.7994
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+ - F1 Macro: 0.7991
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+ - F1 Weighted: 0.7990
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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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+ | 1.086 | 1.0 | 72 | 0.7935 | 0.6887 | 0.6938 | 0.6887 | 0.6884 | 0.6882 |
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+ | 0.8116 | 2.0 | 144 | 0.6396 | 0.7459 | 0.7595 | 0.7454 | 0.7452 | 0.7453 |
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+ | 0.4754 | 3.0 | 216 | 0.5853 | 0.7796 | 0.7812 | 0.7801 | 0.7794 | 0.7793 |
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+ | 0.3729 | 4.0 | 288 | 0.6464 | 0.7796 | 0.7827 | 0.7796 | 0.7795 | 0.7795 |
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+ | 0.2206 | 5.0 | 360 | 0.6963 | 0.7845 | 0.7880 | 0.7842 | 0.7842 | 0.7843 |
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+ | 0.161 | 6.0 | 432 | 0.7742 | 0.7827 | 0.7876 | 0.7829 | 0.7827 | 0.7826 |
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+ | 0.1193 | 7.0 | 504 | 0.8773 | 0.7876 | 0.7906 | 0.7881 | 0.7873 | 0.7871 |
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+ | 0.0858 | 8.0 | 576 | 0.9057 | 0.7800 | 0.7821 | 0.7797 | 0.7801 | 0.7801 |
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+ | 0.081 | 9.0 | 648 | 0.9375 | 0.7871 | 0.7897 | 0.7875 | 0.7872 | 0.7871 |
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+ | 0.0528 | 10.0 | 720 | 0.9239 | 0.7867 | 0.7880 | 0.7869 | 0.7868 | 0.7867 |
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+ | 0.0459 | 11.0 | 792 | 0.9642 | 0.7920 | 0.7928 | 0.7920 | 0.7921 | 0.7921 |
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+ | 0.0319 | 12.0 | 864 | 1.0246 | 0.7951 | 0.7960 | 0.7951 | 0.7952 | 0.7952 |
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+ | 0.0303 | 13.0 | 936 | 1.0632 | 0.7960 | 0.7968 | 0.7963 | 0.7960 | 0.7958 |
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+ | 0.0235 | 14.0 | 1008 | 1.0870 | 0.7956 | 0.7957 | 0.7955 | 0.7956 | 0.7955 |
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+ | 0.0182 | 15.0 | 1080 | 1.1385 | 0.7960 | 0.7965 | 0.7963 | 0.7960 | 0.7959 |
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+ | 0.0114 | 16.0 | 1152 | 1.1546 | 0.8013 | 0.8021 | 0.8012 | 0.8013 | 0.8013 |
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+ | 0.0119 | 17.0 | 1224 | 1.1719 | 0.7969 | 0.7974 | 0.7972 | 0.7969 | 0.7968 |
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+ | 0.0111 | 18.0 | 1296 | 1.1749 | 0.7960 | 0.7966 | 0.7963 | 0.7959 | 0.7958 |
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+ | 0.0067 | 19.0 | 1368 | 1.1719 | 0.7991 | 0.7995 | 0.7994 | 0.7991 | 0.7990 |
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+ | 0.0151 | 20.0 | 1440 | 1.1699 | 0.7991 | 0.7995 | 0.7994 | 0.7991 | 0.7990 |
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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 0.82 0.75 0.79 750
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+ contradiction 0.76 0.76 0.76 737
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+ neutral 0.76 0.82 0.79 777
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+
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+ accuracy 0.78 2264
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+ macro avg 0.78 0.78 0.78 2264
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+ weighted avg 0.78 0.78 0.78 2264
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+
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+ Confusion matrix:
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+ [[565 93 92]
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+ [ 65 560 112]
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+ [ 59 83 635]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,565,93,92
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+ contradiction,65,560,112
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+ neutral,59,83,635
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model_predict.csv ADDED
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