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---
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library_name: transformers
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license: apache-2.0
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base_model: bert-base-uncased
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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-bert-base-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-bert-base-classifier-hptuning
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8018
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- Macro F1: 0.7701
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- Precision: 0.7496
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- Recall: 0.8020
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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: 3.189891002979603e-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: 0.19371368369975006
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- num_epochs: 2
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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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| 2.0456 | 1.0 | 821 | 0.9006 | 0.7267 | 0.6845 | 0.7944 |
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| 0.6691 | 2.0 | 1642 | 0.8018 | 0.7701 | 0.7496 | 0.8020 |
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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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