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--- |
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license: mit |
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base_model: vicgalle/xlm-roberta-large-xnli-anli |
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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: xlm-roberta-large-xnli-anli-v5.0 |
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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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# xlm-roberta-large-xnli-anli-v5.0 |
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This model is a fine-tuned version of [vicgalle/xlm-roberta-large-xnli-anli](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5120 |
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- F1 Macro: 0.8215 |
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- F1 Micro: 0.8223 |
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- Accuracy Balanced: 0.8216 |
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- Accuracy: 0.8223 |
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- Precision Macro: 0.8215 |
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- Recall Macro: 0.8216 |
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- Precision Micro: 0.8223 |
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- Recall Micro: 0.8223 |
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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: 9e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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- seed: 40 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.3779 | 0.85 | 200 | 0.4494 | 0.8020 | 0.8020 | 0.8084 | 0.8020 | 0.8088 | 0.8084 | 0.8020 | 0.8020 | |
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| 0.2646 | 1.69 | 400 | 0.4425 | 0.8113 | 0.8121 | 0.8126 | 0.8121 | 0.8108 | 0.8126 | 0.8121 | 0.8121 | |
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| 0.1961 | 2.54 | 600 | 0.5222 | 0.8131 | 0.8147 | 0.8129 | 0.8147 | 0.8135 | 0.8129 | 0.8147 | 0.8147 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 2.14.7 |
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- Tokenizers 0.13.3 |
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