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ModernBERT-base_nli

Text Classification
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Safetensors
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-base
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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-base_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-base_nli
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+
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+ This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.4416
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+ - Accuracy: 0.5623
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+ - Precision Macro: 0.5618
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+ - Recall Macro: 0.5627
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+ - F1 Macro: 0.5621
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+ - F1 Weighted: 0.5617
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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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+ | 2.164 | 1.0 | 72 | 1.0434 | 0.4483 | 0.4472 | 0.4484 | 0.4398 | 0.4395 |
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+ | 2.0623 | 2.0 | 144 | 0.9968 | 0.4984 | 0.5026 | 0.4994 | 0.4983 | 0.4978 |
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+ | 1.8507 | 3.0 | 216 | 1.0155 | 0.5016 | 0.5522 | 0.5034 | 0.4808 | 0.4802 |
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+ | 1.7076 | 4.0 | 288 | 0.9344 | 0.5721 | 0.5902 | 0.5738 | 0.5572 | 0.5563 |
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+ | 1.4431 | 5.0 | 360 | 0.9258 | 0.5756 | 0.5770 | 0.5768 | 0.5719 | 0.5714 |
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+ | 1.1592 | 6.0 | 432 | 1.0425 | 0.5738 | 0.5831 | 0.5740 | 0.5693 | 0.5691 |
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+ | 0.6916 | 7.0 | 504 | 1.2622 | 0.5659 | 0.5711 | 0.5670 | 0.5640 | 0.5636 |
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+ | 0.3547 | 8.0 | 576 | 1.7560 | 0.5455 | 0.5495 | 0.5452 | 0.5460 | 0.5459 |
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+ | 0.2534 | 9.0 | 648 | 2.1882 | 0.5494 | 0.5620 | 0.5515 | 0.5409 | 0.5401 |
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+ | 0.1018 | 10.0 | 720 | 2.3462 | 0.5645 | 0.5641 | 0.5652 | 0.5633 | 0.5630 |
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+ | 0.0931 | 11.0 | 792 | 2.6256 | 0.5565 | 0.5619 | 0.5582 | 0.5483 | 0.5475 |
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+ | 0.0504 | 12.0 | 864 | 2.7252 | 0.5552 | 0.5570 | 0.5557 | 0.5555 | 0.5551 |
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+ | 0.0379 | 13.0 | 936 | 2.9577 | 0.5517 | 0.5518 | 0.5521 | 0.5518 | 0.5515 |
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+ | 0.0111 | 14.0 | 1008 | 3.2048 | 0.5614 | 0.5621 | 0.5621 | 0.5609 | 0.5604 |
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+ | 0.0018 | 15.0 | 1080 | 3.3005 | 0.5610 | 0.5621 | 0.5612 | 0.5616 | 0.5613 |
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+ | 0.0003 | 16.0 | 1152 | 3.3958 | 0.5610 | 0.5602 | 0.5615 | 0.5605 | 0.5601 |
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+ | 0.0001 | 17.0 | 1224 | 3.4259 | 0.5623 | 0.5617 | 0.5628 | 0.5620 | 0.5617 |
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+ | 0.0001 | 18.0 | 1296 | 3.4368 | 0.5619 | 0.5613 | 0.5623 | 0.5616 | 0.5612 |
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+ | 0.0001 | 19.0 | 1368 | 3.4412 | 0.5619 | 0.5614 | 0.5623 | 0.5616 | 0.5613 |
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+ | 0.0001 | 20.0 | 1440 | 3.4416 | 0.5623 | 0.5618 | 0.5627 | 0.5621 | 0.5617 |
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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 0.57 0.67 0.62 750
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+ contradiction 0.60 0.46 0.52 737
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+ neutral 0.60 0.64 0.62 777
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+
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+ accuracy 0.59 2264
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+ macro avg 0.59 0.59 0.59 2264
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+ weighted avg 0.59 0.59 0.59 2264
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+
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+ Confusion matrix:
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+ [[502 112 136]
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+ [207 338 192]
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+ [166 115 496]]
confusion_matrix_test.csv ADDED
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+ ,entailment,contradiction,neutral
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+ entailment,502,112,136
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+ contradiction,207,338,192
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+ neutral,166,115,496
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model_predict.csv ADDED
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