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---
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license: mit
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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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- f1
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- accuracy
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model-index:
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- name: RoBERTa_token_classification_AraiEval24_Eng_multi_n_dupl
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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_token_classification_AraiEval24_Eng_multi_n_dupl
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6921
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- Precision: 0.1617
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- Recall: 0.0919
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- F1: 0.1172
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- Accuracy: 0.6855
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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: 8
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- eval_batch_size: 8
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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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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 1.3253 | 1.0 | 617 | 1.3214 | 0.1630 | 0.0115 | 0.0216 | 0.7059 |
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| 1.1069 | 2.0 | 1234 | 1.2762 | 0.1354 | 0.0299 | 0.0490 | 0.7012 |
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| 0.9809 | 3.0 | 1851 | 1.3347 | 0.1268 | 0.0614 | 0.0827 | 0.6621 |
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| 0.8247 | 4.0 | 2468 | 1.4661 | 0.1354 | 0.0572 | 0.0804 | 0.6672 |
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| 0.5789 | 5.0 | 3085 | 1.4868 | 0.1434 | 0.0593 | 0.0839 | 0.6698 |
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| 0.4944 | 6.0 | 3702 | 1.5318 | 0.1525 | 0.0829 | 0.1074 | 0.6845 |
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| 0.445 | 7.0 | 4319 | 1.6190 | 0.1608 | 0.0808 | 0.1076 | 0.6882 |
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| 0.4139 | 8.0 | 4936 | 1.6784 | 0.1736 | 0.0945 | 0.1224 | 0.6906 |
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| 0.3402 | 9.0 | 5553 | 1.6696 | 0.1599 | 0.0934 | 0.1180 | 0.6813 |
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| 0.3125 | 10.0 | 6170 | 1.6921 | 0.1617 | 0.0919 | 0.1172 | 0.6855 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.13.3
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