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            ---
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            license: mit
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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: microsoft-deberta-v3-large_cls_SentEval-CR
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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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            # microsoft-deberta-v3-large_cls_SentEval-CR
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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: 0.2799
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            - Accuracy: 0.9363
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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: 4e-05
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            - train_batch_size: 16
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            - eval_batch_size: 16
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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: cosine
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            - lr_scheduler_warmup_ratio: 0.2
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            - num_epochs: 5
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            - mixed_precision_training: Native AMP
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            ### Training results
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            | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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            |:-------------:|:-----:|:----:|:---------------:|:--------:|
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            | No log        | 1.0   | 189  | 0.3706          | 0.9110   |
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            | No log        | 2.0   | 378  | 0.2884          | 0.9243   |
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            | 0.3597        | 3.0   | 567  | 0.3018          | 0.9177   |
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            | 0.3597        | 4.0   | 756  | 0.1996          | 0.9363   |
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            | 0.3597        | 5.0   | 945  | 0.2799          | 0.9363   |
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            ### Framework versions
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            - Transformers 4.20.1
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            - Pytorch 1.11.0
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            - Datasets 2.1.0
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            - Tokenizers 0.12.1
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