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---
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library_name: transformers
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license: mit
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base_model: roberta-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-roberta-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-roberta-classifier-hptuning
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9127
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- Macro F1: 0.7923
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- Precision: 0.7838
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- Recall: 0.8086
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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: 2.286699715088989e-05
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- train_batch_size: 8
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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: 0.1305287632322581
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- num_epochs: 3
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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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| 1.741 | 1.0 | 1641 | 0.8669 | 0.7549 | 0.7408 | 0.7921 |
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| 0.7331 | 2.0 | 3282 | 0.8423 | 0.7804 | 0.7676 | 0.8016 |
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| 0.4616 | 3.0 | 4923 | 0.9127 | 0.7923 | 0.7838 | 0.8086 |
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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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