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phobert-large_nli

Text Classification
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roberta
Generated from Trainer
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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-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_nli
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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_nli
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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: 1.3062
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+ - Accuracy: 0.8102
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+ - Precision Macro: 0.8106
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+ - Recall Macro: 0.8103
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+ - F1 Macro: 0.8103
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+ - F1 Weighted: 0.8103
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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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+ | 1.0976 | 1.0 | 72 | 1.0257 | 0.5237 | 0.5529 | 0.5264 | 0.5082 | 0.5072 |
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+ | 0.9271 | 2.0 | 144 | 0.6649 | 0.7592 | 0.7887 | 0.7579 | 0.7590 | 0.7590 |
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+ | 0.4037 | 3.0 | 216 | 0.5864 | 0.7894 | 0.7930 | 0.7895 | 0.7895 | 0.7895 |
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+ | 0.2866 | 4.0 | 288 | 0.6385 | 0.8120 | 0.8142 | 0.8125 | 0.8118 | 0.8118 |
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+ | 0.1197 | 5.0 | 360 | 0.6949 | 0.8115 | 0.8117 | 0.8115 | 0.8115 | 0.8115 |
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+ | 0.0939 | 6.0 | 432 | 0.7485 | 0.8058 | 0.8084 | 0.8060 | 0.8058 | 0.8059 |
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+ | 0.0647 | 7.0 | 504 | 0.9244 | 0.7920 | 0.7977 | 0.7921 | 0.7919 | 0.7918 |
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+ | 0.0457 | 8.0 | 576 | 0.8464 | 0.8106 | 0.8107 | 0.8107 | 0.8106 | 0.8106 |
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+ | 0.046 | 9.0 | 648 | 0.9886 | 0.8062 | 0.8121 | 0.8066 | 0.8064 | 0.8063 |
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+ | 0.026 | 10.0 | 720 | 0.9887 | 0.8120 | 0.8126 | 0.8121 | 0.8120 | 0.8121 |
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+ | 0.0244 | 11.0 | 792 | 1.0642 | 0.8124 | 0.8130 | 0.8126 | 0.8125 | 0.8125 |
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+ | 0.0211 | 12.0 | 864 | 1.0197 | 0.8075 | 0.8097 | 0.8078 | 0.8077 | 0.8077 |
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+ | 0.0146 | 13.0 | 936 | 1.1487 | 0.8151 | 0.8171 | 0.8155 | 0.8151 | 0.8151 |
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+ | 0.0085 | 14.0 | 1008 | 1.1846 | 0.8053 | 0.8056 | 0.8053 | 0.8053 | 0.8053 |
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+ | 0.0051 | 15.0 | 1080 | 1.2905 | 0.8084 | 0.8095 | 0.8085 | 0.8084 | 0.8084 |
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+ | 0.0036 | 16.0 | 1152 | 1.3259 | 0.8102 | 0.8121 | 0.8104 | 0.8104 | 0.8104 |
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+ | 0.0027 | 17.0 | 1224 | 1.3187 | 0.8115 | 0.8121 | 0.8115 | 0.8116 | 0.8116 |
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+ | 0.0023 | 18.0 | 1296 | 1.3024 | 0.8115 | 0.8120 | 0.8117 | 0.8116 | 0.8116 |
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+ | 0.0025 | 19.0 | 1368 | 1.3049 | 0.8111 | 0.8115 | 0.8112 | 0.8111 | 0.8111 |
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+ | 0.0037 | 20.0 | 1440 | 1.3062 | 0.8102 | 0.8106 | 0.8103 | 0.8103 | 0.8103 |
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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 0.78 0.84 0.81 750
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+ contradiction 0.81 0.72 0.77 737
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+ neutral 0.80 0.83 0.81 777
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+
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+ accuracy 0.80 2264
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+ macro avg 0.80 0.80 0.80 2264
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+ weighted avg 0.80 0.80 0.80 2264
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+
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+ Confusion matrix:
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+ [[633 57 60]
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+ [105 534 98]
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+ [ 70 65 642]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,633,57,60
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+ contradiction,105,534,98
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+ neutral,70,65,642
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model_predict.csv ADDED
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