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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: 0.2358 |
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- Accuracy: 0.9580 |
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- Precision: 0.9583 |
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- Recall: 0.9578 |
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- F1: 0.9580 |
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- Ratio: 0.4803 |
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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: 3 |
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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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| 0.5219 | 0.43 | 400 | 0.3524 | 0.8954 | 0.8972 | 0.8946 | 0.8951 | 0.4577 | |
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| 0.4069 | 0.86 | 800 | 0.3178 | 0.9249 | 0.9250 | 0.9246 | 0.9248 | 0.4809 | |
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| 0.2326 | 1.29 | 1200 | 0.3055 | 0.9355 | 0.9360 | 0.9351 | 0.9354 | 0.4740 | |
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| 0.2045 | 1.72 | 1600 | 0.2847 | 0.9455 | 0.9457 | 0.9453 | 0.9455 | 0.4803 | |
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| 0.1423 | 2.15 | 2000 | 0.2477 | 0.9555 | 0.9555 | 0.9556 | 0.9555 | 0.4903 | |
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| 0.0935 | 2.58 | 2400 | 0.2367 | 0.9599 | 0.9598 | 0.9600 | 0.9599 | 0.4922 | |
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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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