| | ---
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| | base_model: klue/roberta-base
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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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| | model-index:
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| | - name: roberta-interview-intent
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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
|
| | should probably proofread and complete it, then remove this comment. -->
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| |
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| | # roberta-interview-intent
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| |
|
| | This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the None dataset.
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| | It achieves the following results on the evaluation set:
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| | - Loss: 1.8618
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| | - Accuracy: 0.6771
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| | - Macro F1: 0.4469
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| | - Weighted F1: 0.6798
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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: 32
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| | - eval_batch_size: 32
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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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| | - lr_scheduler_warmup_ratio: 0.06
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| | - num_epochs: 10
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| | - mixed_precision_training: Native AMP
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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 | Macro F1 | Weighted F1 |
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| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
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| | | 1.8294 | 1.0 | 859 | 1.4177 | 0.6630 | 0.3062 | 0.6381 |
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| | | 0.7746 | 2.0 | 1718 | 1.3270 | 0.6777 | 0.3558 | 0.6590 |
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| | | 0.5419 | 3.0 | 2577 | 1.3263 | 0.6806 | 0.4224 | 0.6715 |
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| | | 0.3855 | 4.0 | 3436 | 1.4520 | 0.6775 | 0.4342 | 0.6726 |
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| | | 0.2805 | 5.0 | 4295 | 1.5418 | 0.6775 | 0.4364 | 0.6767 |
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| | | 0.2026 | 6.0 | 5154 | 1.5926 | 0.6734 | 0.4439 | 0.6771 |
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| | | 0.1448 | 7.0 | 6013 | 1.7215 | 0.6775 | 0.4451 | 0.6802 |
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| | | 0.106 | 8.0 | 6872 | 1.8030 | 0.6728 | 0.4492 | 0.6781 |
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| | | 0.0778 | 9.0 | 7731 | 1.8198 | 0.6806 | 0.4518 | 0.6828 |
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| | | 0.0611 | 10.0 | 8590 | 1.8618 | 0.6771 | 0.4469 | 0.6798 |
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| |
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| | ### Framework versions
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| |
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| | - Transformers 4.40.2
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| | - Pytorch 2.8.0+cu128
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| | - Datasets 2.19.0
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| | - Tokenizers 0.19.1
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| | |
