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roberta-large-mnli_nli

Text Classification
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Safetensors
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_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: FacebookAI/roberta-large-mnli
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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: roberta-large-mnli_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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+ # roberta-large-mnli_nli
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+
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+ This model is a fine-tuned version of [FacebookAI/roberta-large-mnli](https://huggingface.co/FacebookAI/roberta-large-mnli) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.9476
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+ - Accuracy: 0.6009
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+ - Precision Macro: 0.6028
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+ - Recall Macro: 0.6009
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+ - F1 Macro: 0.6014
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+ - F1 Weighted: 0.6012
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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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+ | 1.0485 | 1.0 | 143 | 0.9848 | 0.5162 | 0.5491 | 0.5191 | 0.4775 | 0.4758 |
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+ | 0.9114 | 2.0 | 286 | 0.9839 | 0.5264 | 0.5642 | 0.5266 | 0.5150 | 0.5148 |
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+ | 0.8746 | 3.0 | 429 | 0.9618 | 0.5517 | 0.5743 | 0.5522 | 0.5453 | 0.5451 |
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+ | 0.7909 | 4.0 | 572 | 0.9498 | 0.5805 | 0.5859 | 0.5813 | 0.5766 | 0.5762 |
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+ | 0.7105 | 5.0 | 715 | 0.9324 | 0.5956 | 0.6000 | 0.5960 | 0.5939 | 0.5936 |
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+ | 0.6205 | 6.0 | 858 | 0.9797 | 0.5933 | 0.5958 | 0.5934 | 0.5927 | 0.5925 |
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+ | 0.5113 | 7.0 | 1001 | 1.1925 | 0.5889 | 0.5918 | 0.5896 | 0.5857 | 0.5853 |
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+ | 0.4181 | 8.0 | 1144 | 1.2665 | 0.5916 | 0.5922 | 0.5918 | 0.5918 | 0.5916 |
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+ | 0.3218 | 9.0 | 1287 | 1.4587 | 0.5849 | 0.5866 | 0.5848 | 0.5849 | 0.5849 |
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+ | 0.2543 | 10.0 | 1430 | 1.5554 | 0.5902 | 0.5910 | 0.5908 | 0.5892 | 0.5889 |
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+ | 0.1851 | 11.0 | 1573 | 1.8125 | 0.5787 | 0.5829 | 0.5782 | 0.5786 | 0.5787 |
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+ | 0.1316 | 12.0 | 1716 | 2.0182 | 0.5827 | 0.5837 | 0.5826 | 0.5826 | 0.5825 |
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+ | 0.0884 | 13.0 | 1859 | 2.1233 | 0.5809 | 0.5823 | 0.5810 | 0.5812 | 0.5811 |
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+ | 0.0708 | 14.0 | 2002 | 2.2924 | 0.5938 | 0.5936 | 0.5943 | 0.5935 | 0.5931 |
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+ | 0.0527 | 15.0 | 2145 | 2.4595 | 0.5916 | 0.5923 | 0.5919 | 0.5918 | 0.5916 |
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+ | 0.0334 | 16.0 | 2288 | 2.6315 | 0.5991 | 0.6009 | 0.5991 | 0.5996 | 0.5995 |
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+ | 0.0186 | 17.0 | 2431 | 2.8367 | 0.5947 | 0.5979 | 0.5946 | 0.5953 | 0.5952 |
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+ | 0.0179 | 18.0 | 2574 | 2.9197 | 0.6004 | 0.6032 | 0.6004 | 0.6010 | 0.6009 |
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+ | 0.0113 | 19.0 | 2717 | 2.9423 | 0.5982 | 0.6003 | 0.5982 | 0.5987 | 0.5986 |
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+ | 0.0134 | 20.0 | 2860 | 2.9476 | 0.6009 | 0.6028 | 0.6009 | 0.6014 | 0.6012 |
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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 0.61 0.61 0.61 750
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+ contradiction 0.54 0.57 0.55 737
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+ neutral 0.64 0.62 0.63 777
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+
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+ accuracy 0.60 2264
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+ macro avg 0.60 0.60 0.60 2264
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+ weighted avg 0.60 0.60 0.60 2264
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+
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+ Confusion matrix:
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+ [[454 180 116]
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+ [162 417 158]
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+ [124 172 481]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,454,180,116
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+ contradiction,162,417,158
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+ neutral,124,172,481
model_predict.csv ADDED
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