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vit5-large_nli

Text Classification
Transformers
Safetensors
t5
Generated from Trainer
text-generation-inference
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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: VietAI/vit5-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: vit5-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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+ # vit5-large_nli
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+
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+ This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8846
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+ - Accuracy: 0.8018
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+ - Precision Macro: 0.8019
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+ - Recall Macro: 0.8020
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+ - F1 Macro: 0.8018
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+ - F1 Weighted: 0.8017
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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: 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: 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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+ | 1.0755 | 1.0 | 143 | 0.7110 | 0.7020 | 0.7065 | 0.7026 | 0.7008 | 0.7007 |
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+ | 0.5707 | 2.0 | 286 | 0.5831 | 0.7800 | 0.7892 | 0.7793 | 0.7800 | 0.7801 |
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+ | 0.3356 | 3.0 | 429 | 0.6000 | 0.7911 | 0.7927 | 0.7916 | 0.7910 | 0.7909 |
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+ | 0.1497 | 4.0 | 572 | 0.7687 | 0.7827 | 0.7848 | 0.7830 | 0.7826 | 0.7825 |
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+ | 0.0876 | 5.0 | 715 | 0.8672 | 0.7867 | 0.7892 | 0.7864 | 0.7868 | 0.7868 |
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+ | 0.0601 | 6.0 | 858 | 1.1073 | 0.7863 | 0.7869 | 0.7862 | 0.7862 | 0.7862 |
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+ | 0.0473 | 7.0 | 1001 | 1.2264 | 0.7769 | 0.7821 | 0.7777 | 0.7762 | 0.7760 |
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+ | 0.037 | 8.0 | 1144 | 1.1917 | 0.7947 | 0.7956 | 0.7945 | 0.7948 | 0.7948 |
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+ | 0.0256 | 9.0 | 1287 | 1.3581 | 0.7867 | 0.7869 | 0.7866 | 0.7867 | 0.7867 |
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+ | 0.0188 | 10.0 | 1430 | 1.3638 | 0.7916 | 0.7919 | 0.7916 | 0.7915 | 0.7916 |
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+ | 0.0153 | 11.0 | 1573 | 1.5960 | 0.7902 | 0.7914 | 0.7903 | 0.7903 | 0.7903 |
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+ | 0.0101 | 12.0 | 1716 | 1.6123 | 0.7938 | 0.7938 | 0.7939 | 0.7938 | 0.7937 |
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+ | 0.0098 | 13.0 | 1859 | 1.7553 | 0.8 | 0.8017 | 0.8004 | 0.7999 | 0.7999 |
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+ | 0.006 | 14.0 | 2002 | 1.7906 | 0.7978 | 0.7985 | 0.7982 | 0.7977 | 0.7975 |
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+ | 0.0047 | 15.0 | 2145 | 1.8154 | 0.7991 | 0.7992 | 0.7993 | 0.7991 | 0.7991 |
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+ | 0.0034 | 16.0 | 2288 | 1.8285 | 0.8013 | 0.8015 | 0.8016 | 0.8013 | 0.8012 |
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+ | 0.0018 | 17.0 | 2431 | 1.8543 | 0.8004 | 0.8006 | 0.8007 | 0.8004 | 0.8003 |
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+ | 0.0021 | 18.0 | 2574 | 1.8807 | 0.8018 | 0.8019 | 0.8020 | 0.8018 | 0.8017 |
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+ | 0.0009 | 19.0 | 2717 | 1.8842 | 0.8013 | 0.8014 | 0.8015 | 0.8013 | 0.8013 |
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+ | 0.0019 | 20.0 | 2860 | 1.8846 | 0.8018 | 0.8019 | 0.8020 | 0.8018 | 0.8017 |
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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 0.82 0.80 750
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+ contradiction 0.79 0.76 0.78 737
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+ neutral 0.79 0.79 0.79 777
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+
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+ accuracy 0.79 2264
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+ macro avg 0.79 0.79 0.79 2264
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+ weighted avg 0.79 0.79 0.79 2264
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+
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+ Confusion matrix:
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+ [[614 62 74]
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+ [ 92 560 85]
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+ [ 82 85 610]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,614,62,74
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+ contradiction,92,560,85
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+ neutral,82,85,610
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model_predict.csv ADDED
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