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--- |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-energy |
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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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# mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-energy |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2328 |
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- Accuracy: 0.9637 |
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- Precision: 0.9637 |
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- Recall: 0.9636 |
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- F1: 0.9637 |
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- Ratio: 0.4847 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.06 |
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- lr_scheduler_warmup_steps: 3 |
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- num_epochs: 5 |
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- label_smoothing_factor: 0.01 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 0.5212 | 0.43 | 400 | 0.3449 | 0.8948 | 0.8964 | 0.8940 | 0.8945 | 0.4596 | |
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| 0.4083 | 0.86 | 800 | 0.3203 | 0.9224 | 0.9232 | 0.9218 | 0.9222 | 0.4684 | |
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| 0.2384 | 1.29 | 1200 | 0.3149 | 0.9361 | 0.9365 | 0.9358 | 0.9360 | 0.4759 | |
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| 0.213 | 1.72 | 1600 | 0.3024 | 0.9443 | 0.9442 | 0.9442 | 0.9442 | 0.4865 | |
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| 0.1686 | 2.15 | 2000 | 0.2742 | 0.9493 | 0.6332 | 0.6329 | 0.6330 | 0.4934 | |
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| 0.105 | 2.58 | 2400 | 0.2641 | 0.9518 | 0.9519 | 0.9522 | 0.9518 | 0.5041 | |
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| 0.116 | 3.01 | 2800 | 0.2515 | 0.9555 | 0.6374 | 0.6372 | 0.6372 | 0.4997 | |
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| 0.077 | 3.44 | 3200 | 0.2511 | 0.9580 | 0.9580 | 0.9583 | 0.9580 | 0.4966 | |
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| 0.0622 | 3.86 | 3600 | 0.2355 | 0.9643 | 0.9644 | 0.9642 | 0.9643 | 0.4828 | |
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| 0.0524 | 4.29 | 4000 | 0.2289 | 0.9637 | 0.9636 | 0.9637 | 0.9637 | 0.4884 | |
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| 0.0498 | 4.72 | 4400 | 0.2336 | 0.9643 | 0.9644 | 0.9642 | 0.9643 | 0.4840 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.7 |
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- Tokenizers 0.13.3 |
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