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---
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library_name: transformers
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license: mit
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base_model: microsoft/deberta-v3-xsmall
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tags:
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- generated_from_trainer
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model-index:
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- name: unique-ape-807
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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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# unique-ape-807
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This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1850
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- Hamming Loss: 0.0497
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- Zero One Loss: 1.0
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- Jaccard Score: 1.0
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- Hamming Loss Optimised: 0.0497
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- Hamming Loss Threshold: 0.9000
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- Zero One Loss Optimised: 1.0
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- Zero One Loss Threshold: 0.9000
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- Jaccard Score Optimised: 1.0
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- Jaccard Score Threshold: 0.9000
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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: 5.0943791435964314e-05
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- train_batch_size: 20
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- eval_batch_size: 20
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- seed: 2024
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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: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
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| No log | 1.0 | 160 | 0.1872 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| No log | 2.0 | 320 | 0.1850 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 2.5.1+cu118
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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