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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-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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model-index:
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- name: lifechart-deberta-classifier-hptuning
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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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# lifechart-deberta-classifier-hptuning
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9622
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- Macro F1: 0.7854
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- Precision: 0.7750
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- Recall: 0.8009
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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: 2.0260649431134323e-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: Use OptimizerNames.ADAMW_TORCH_FUSED 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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- lr_scheduler_warmup_ratio: 0.09915082219848009
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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| 2.0427 | 1.0 | 821 | 0.8925 | 0.7133 | 0.6744 | 0.7897 |
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| 0.7423 | 2.0 | 1642 | 0.7529 | 0.7677 | 0.7333 | 0.8192 |
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| 0.4454 | 3.0 | 2463 | 0.8392 | 0.7721 | 0.7592 | 0.7980 |
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| 0.2746 | 4.0 | 3284 | 0.9407 | 0.7711 | 0.7626 | 0.7873 |
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| 0.1817 | 5.0 | 4105 | 0.9622 | 0.7854 | 0.7750 | 0.8009 |
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### Framework versions
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- Transformers 4.55.4
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- Pytorch 2.8.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.21.4
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