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CafeBERT_nli

Text Classification
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Safetensors
xlm-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: apache-2.0
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+ base_model: uitnlp/CafeBERT
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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: CafeBERT_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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+ # CafeBERT_nli
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+
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+ This model is a fine-tuned version of [uitnlp/CafeBERT](https://huggingface.co/uitnlp/CafeBERT) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2989
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+ - Accuracy: 0.8306
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+ - Precision Macro: 0.8307
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+ - Recall Macro: 0.8308
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+ - F1 Macro: 0.8306
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+ - F1 Weighted: 0.8306
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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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+ | 1.0641 | 1.0 | 72 | 0.6313 | 0.7565 | 0.7672 | 0.7575 | 0.7562 | 0.7561 |
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+ | 0.64 | 2.0 | 144 | 0.5313 | 0.8044 | 0.8077 | 0.8042 | 0.8039 | 0.8040 |
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+ | 0.3679 | 3.0 | 216 | 0.5117 | 0.8062 | 0.8078 | 0.8067 | 0.8060 | 0.8059 |
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+ | 0.2855 | 4.0 | 288 | 0.5816 | 0.8098 | 0.8150 | 0.8101 | 0.8087 | 0.8087 |
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+ | 0.1571 | 5.0 | 360 | 0.6372 | 0.8058 | 0.8060 | 0.8058 | 0.8058 | 0.8059 |
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+ | 0.1165 | 6.0 | 432 | 0.6929 | 0.8177 | 0.8186 | 0.8177 | 0.8178 | 0.8178 |
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+ | 0.0855 | 7.0 | 504 | 0.7374 | 0.8084 | 0.8090 | 0.8087 | 0.8084 | 0.8084 |
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+ | 0.0704 | 8.0 | 576 | 0.8241 | 0.8075 | 0.8107 | 0.8071 | 0.8075 | 0.8075 |
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+ | 0.0593 | 9.0 | 648 | 0.9712 | 0.8098 | 0.8108 | 0.8094 | 0.8095 | 0.8096 |
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+ | 0.0415 | 10.0 | 720 | 0.8643 | 0.8155 | 0.8165 | 0.8153 | 0.8155 | 0.8155 |
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+ | 0.034 | 11.0 | 792 | 0.9662 | 0.8124 | 0.8149 | 0.8120 | 0.8123 | 0.8123 |
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+ | 0.0273 | 12.0 | 864 | 1.0114 | 0.8182 | 0.8188 | 0.8181 | 0.8182 | 0.8182 |
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+ | 0.0189 | 13.0 | 936 | 1.2237 | 0.8155 | 0.8195 | 0.8159 | 0.8156 | 0.8155 |
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+ | 0.0068 | 14.0 | 1008 | 1.2312 | 0.8244 | 0.8265 | 0.8247 | 0.8244 | 0.8244 |
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+ | 0.011 | 15.0 | 1080 | 1.2062 | 0.8315 | 0.8316 | 0.8316 | 0.8314 | 0.8314 |
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+ | 0.003 | 16.0 | 1152 | 1.2550 | 0.8279 | 0.8280 | 0.8280 | 0.8280 | 0.8279 |
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+ | 0.0024 | 17.0 | 1224 | 1.2774 | 0.8302 | 0.8303 | 0.8303 | 0.8302 | 0.8302 |
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+ | 0.003 | 18.0 | 1296 | 1.2946 | 0.8293 | 0.8295 | 0.8295 | 0.8292 | 0.8292 |
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+ | 0.0023 | 19.0 | 1368 | 1.2969 | 0.8306 | 0.8307 | 0.8308 | 0.8306 | 0.8306 |
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+ | 0.0012 | 20.0 | 1440 | 1.2989 | 0.8306 | 0.8307 | 0.8308 | 0.8306 | 0.8306 |
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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 0.83 0.85 0.84 750
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+ contradiction 0.79 0.82 0.80 737
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+ neutral 0.83 0.78 0.81 777
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+
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+ accuracy 0.82 2264
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+ macro avg 0.82 0.82 0.82 2264
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+ weighted avg 0.82 0.82 0.82 2264
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+
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+ Confusion matrix:
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+ [[634 62 54]
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+ [ 64 603 70]
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+ [ 68 100 609]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,634,62,54
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+ contradiction,64,603,70
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+ neutral,68,100,609
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model_predict.csv ADDED
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