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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-small
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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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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: DeBERTaV3_model_V3_multilabel
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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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+ # DeBERTaV3_model_V3_multilabel
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0117
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+ - Accuracy: 0.9978
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+ - F1: 0.9985
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+ - Precision: 0.9970
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+ - Recall: 1.0
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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: 2e-05
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+ - train_batch_size: 5
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+ - eval_batch_size: 5
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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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+ - num_epochs: 13
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 90 | 0.0239 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | No log | 2.0 | 180 | 0.0147 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | No log | 3.0 | 270 | 0.0131 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | No log | 4.0 | 360 | 0.0124 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | No log | 5.0 | 450 | 0.0121 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 6.0 | 540 | 0.0119 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 7.0 | 630 | 0.0117 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 8.0 | 720 | 0.0120 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 9.0 | 810 | 0.0127 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 10.0 | 900 | 0.0132 | 0.9978 | 0.9985 | 0.9970 | 1.0 |
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+ | 0.0608 | 11.0 | 990 | 0.0139 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
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+ | 0.0121 | 12.0 | 1080 | 0.0143 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
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+ | 0.0121 | 13.0 | 1170 | 0.0143 | 0.9970 | 0.998 | 0.9970 | 0.9990 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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