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README.md
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---
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library_name: transformers
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license: mit
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base_model: roberta-large
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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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- f1
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- precision
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- recall
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model-index:
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- name: roberta-large-ToM4
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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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# roberta-large-ToM4
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3284
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- Accuracy: 0.9425
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- F1: 0.8387
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- Precision: 0.8667
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- Recall: 0.8125
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 2015
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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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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.4261 | 1.0 | 93 | 0.3464 | 0.8333 | 0.6829 | 0.5385 | 0.9333 |
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| 0.1804 | 2.0 | 186 | 0.3276 | 0.8846 | 0.7097 | 0.6875 | 0.7333 |
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| 0.1334 | 3.0 | 279 | 0.3743 | 0.8846 | 0.7097 | 0.6875 | 0.7333 |
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| 0.089 | 4.0 | 372 | 0.5870 | 0.8974 | 0.7333 | 0.7333 | 0.7333 |
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| 0.0542 | 5.0 | 465 | 0.5656 | 0.9103 | 0.7586 | 0.7857 | 0.7333 |
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### Framework versions
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- Transformers 4.56.0
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- Pytorch 2.8.0+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.0
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