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--- |
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license: mit |
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base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- recall |
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- accuracy |
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- precision |
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model-index: |
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- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss |
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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-base-fine-tuned-text-classificarion-ds-ss |
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This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0473 |
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- F1: 0.7427 |
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- Recall: 0.7662 |
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- Accuracy: 0.7662 |
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- Precision: 0.7444 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Accuracy | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:---------:| |
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| 3.4822 | 1.0 | 883 | 2.0495 | 0.3963 | 0.4790 | 0.4790 | 0.3791 | |
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| 1.6347 | 2.0 | 1766 | 1.2672 | 0.6622 | 0.7030 | 0.7030 | 0.6535 | |
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| 1.0807 | 3.0 | 2649 | 1.0711 | 0.7172 | 0.7420 | 0.7420 | 0.7065 | |
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| 0.8958 | 4.0 | 3532 | 1.0654 | 0.7232 | 0.7489 | 0.7489 | 0.7218 | |
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| 0.7766 | 5.0 | 4415 | 1.0473 | 0.7427 | 0.7662 | 0.7662 | 0.7444 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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