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ModernBERT-large_nli

Text Classification
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modernbert
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: answerdotai/ModernBERT-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: ModernBERT-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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+ # ModernBERT-large_nli
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+
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+ This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.6038
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+ - Accuracy: 0.5787
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+ - Precision Macro: 0.5794
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+ - Recall Macro: 0.5790
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+ - F1 Macro: 0.5792
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+ - F1 Weighted: 0.5788
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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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+ | 2.1283 | 1.0 | 143 | 1.0136 | 0.4807 | 0.4674 | 0.4835 | 0.4509 | 0.4492 |
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+ | 1.8848 | 2.0 | 286 | 0.9818 | 0.5202 | 0.5745 | 0.5219 | 0.5042 | 0.5038 |
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+ | 1.7416 | 3.0 | 429 | 1.1233 | 0.3220 | 0.2102 | 0.3259 | 0.2190 | 0.2174 |
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+ | 2.2168 | 4.0 | 572 | 1.1135 | 0.3277 | 0.1092 | 0.3333 | 0.1646 | 0.1618 |
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+ | 2.2099 | 5.0 | 715 | 1.1089 | 0.3277 | 0.1092 | 0.3333 | 0.1646 | 0.1618 |
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+ | 2.2191 | 6.0 | 858 | 1.1231 | 0.3282 | 0.4426 | 0.3338 | 0.1655 | 0.1627 |
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+ | 2.2027 | 7.0 | 1001 | 1.0931 | 0.3774 | 0.2508 | 0.3801 | 0.3016 | 0.2993 |
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+ | 2.1846 | 8.0 | 1144 | 1.0723 | 0.4013 | 0.3861 | 0.3995 | 0.3692 | 0.3705 |
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+ | 2.1232 | 9.0 | 1287 | 1.0461 | 0.4244 | 0.4225 | 0.4248 | 0.4203 | 0.4202 |
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+ | 2.0586 | 10.0 | 1430 | 1.0345 | 0.4510 | 0.4495 | 0.4494 | 0.4210 | 0.4220 |
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+ | 2.0578 | 11.0 | 1573 | 1.0390 | 0.4523 | 0.4797 | 0.4511 | 0.4522 | 0.4525 |
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+ | 2.0289 | 12.0 | 1716 | 1.0626 | 0.4665 | 0.5296 | 0.4668 | 0.4391 | 0.4389 |
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+ | 1.5688 | 13.0 | 1859 | 0.8686 | 0.6084 | 0.6082 | 0.6089 | 0.6064 | 0.6061 |
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+ | 1.2262 | 14.0 | 2002 | 0.9452 | 0.5973 | 0.5972 | 0.5978 | 0.5961 | 0.5958 |
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+ | 0.6694 | 15.0 | 2145 | 1.2849 | 0.5809 | 0.5809 | 0.5817 | 0.5802 | 0.5798 |
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+ | 0.2152 | 16.0 | 2288 | 1.9241 | 0.5752 | 0.5760 | 0.5753 | 0.5755 | 0.5753 |
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+ | 0.043 | 17.0 | 2431 | 2.3196 | 0.5672 | 0.5685 | 0.5673 | 0.5675 | 0.5672 |
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+ | 0.0074 | 18.0 | 2574 | 2.5393 | 0.5734 | 0.5747 | 0.5736 | 0.5740 | 0.5737 |
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+ | 0.0015 | 19.0 | 2717 | 2.5970 | 0.5769 | 0.5780 | 0.5772 | 0.5776 | 0.5772 |
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+ | 0.002 | 20.0 | 2860 | 2.6038 | 0.5787 | 0.5794 | 0.5790 | 0.5792 | 0.5788 |
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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 0.63 0.62 750
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+ contradiction 0.60 0.51 0.55 737
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+ neutral 0.63 0.69 0.66 777
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+
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+ accuracy 0.61 2264
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+ macro avg 0.61 0.61 0.61 2264
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+ weighted avg 0.61 0.61 0.61 2264
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+
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+ Confusion matrix:
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+ [[473 143 134]
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+ [176 376 185]
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+ [128 112 537]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,473,143,134
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+ contradiction,176,376,185
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+ neutral,128,112,537
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model_predict.csv ADDED
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