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--- |
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library_name: transformers |
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license: mit |
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base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 |
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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: nli-cross-encoder-roberta |
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results: [] |
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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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# nli-cross-encoder-roberta |
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This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4859 |
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- Accuracy: 0.9448 |
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- F1 Macro: 0.9469 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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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: 32 |
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- optimizer: Use OptimizerNames.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: 0.06 |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
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| 0.1806 | 1.0 | 211 | 0.3069 | 0.9088 | 0.9134 | |
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| 0.1021 | 2.0 | 422 | 0.1795 | 0.9530 | 0.9544 | |
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| 0.0343 | 3.0 | 633 | 0.4396 | 0.9365 | 0.9389 | |
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| 0.0182 | 4.0 | 844 | 0.4025 | 0.9475 | 0.9496 | |
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| 0.0047 | 5.0 | 1055 | 0.4674 | 0.9420 | 0.9441 | |
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| 0.0014 | 6.0 | 1266 | 0.4457 | 0.9448 | 0.9469 | |
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| 0.0049 | 7.0 | 1477 | 0.4835 | 0.9448 | 0.9469 | |
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| 0.0004 | 8.0 | 1688 | 0.4859 | 0.9448 | 0.9469 | |
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### Framework versions |
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- Transformers 4.56.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.0 |
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