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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-large
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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: deberta-v3-large-zeroshot-v2.0-2024-04-01-09-59
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+ results: []
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+ ---
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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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+
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+ # deberta-v3-large-zeroshot-v2.0-2024-04-01-09-59
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1590
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+ - F1 Macro: 0.6865
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+ - F1 Micro: 0.7525
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+ - Accuracy Balanced: 0.7303
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+ - Accuracy: 0.7525
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+ - Precision Macro: 0.6989
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+ - Recall Macro: 0.7303
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+ - Precision Micro: 0.7525
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+ - Recall Micro: 0.7525
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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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: 2
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+
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+ ### Training results
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+
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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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+ | 0.1929 | 1.0 | 43383 | 0.3181 | 0.8694 | 0.8813 | 0.8688 | 0.8813 | 0.8701 | 0.8688 | 0.8813 | 0.8813 |
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+ | 0.1358 | 2.0 | 86766 | 0.3519 | 0.8730 | 0.8849 | 0.8713 | 0.8849 | 0.8749 | 0.8713 | 0.8849 | 0.8849 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.2+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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