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--- |
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library_name: transformers |
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license: mit |
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base_model: MoritzLaurer/deberta-v3-large-zeroshot-v2.0 |
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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: results |
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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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# results |
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This model is a fine-tuned version of [MoritzLaurer/deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2757 |
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- Accuracy: 0.8113 |
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- F1 Macro: 0.7264 |
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- Precision Neutral: 0.8559 |
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- Recall Neutral: 0.8920 |
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- F1 Neutral: 0.8736 |
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- Support Neutral: 213 |
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- Precision Entailment: 0.7436 |
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- Recall Entailment: 0.5472 |
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- F1 Entailment: 0.6304 |
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- Support Entailment: 53 |
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- Precision Contradiction: 0.6341 |
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- Recall Contradiction: 0.7222 |
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- F1 Contradiction: 0.6753 |
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- Support Contradiction: 36 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 128 |
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- seed: 42 |
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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: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Neutral | Recall Neutral | F1 Neutral | Support Neutral | Precision Entailment | Recall Entailment | F1 Entailment | Support Entailment | Precision Contradiction | Recall Contradiction | F1 Contradiction | Support Contradiction | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:---------------:|:--------------------:|:-----------------:|:-------------:|:------------------:|:-----------------------:|:--------------------:|:----------------:|:---------------------:| |
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| 0.0311 | 1.0 | 383 | 0.2757 | 0.8113 | 0.7264 | 0.8559 | 0.8920 | 0.8736 | 213 | 0.7436 | 0.5472 | 0.6304 | 53 | 0.6341 | 0.7222 | 0.6753 | 36 | |
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| 0.0179 | 2.0 | 766 | 0.3661 | 0.7781 | 0.6858 | 0.8387 | 0.8545 | 0.8465 | 213 | 0.6226 | 0.6226 | 0.6226 | 53 | 0.625 | 0.5556 | 0.5882 | 36 | |
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| 0.003 | 3.0 | 1149 | 0.4953 | 0.8013 | 0.7063 | 0.8341 | 0.8967 | 0.8643 | 213 | 0.7045 | 0.5849 | 0.6392 | 53 | 0.6897 | 0.5556 | 0.6154 | 36 | |
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| 0.0008 | 4.0 | 1532 | 0.5224 | 0.7980 | 0.6984 | 0.8341 | 0.8967 | 0.8643 | 213 | 0.6977 | 0.5660 | 0.625 | 53 | 0.6667 | 0.5556 | 0.6061 | 36 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.1 |
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- Tokenizers 0.21.1 |
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