| | ---
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| | license: mit
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| | base_model: FacebookAI/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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| | model-index:
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| | - name: fine_tuned_main_raid
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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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| | # fine_tuned_main_raid
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| |
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| | This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
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| | It achieves the following results on the evaluation set:
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| | - Loss: 0.0505
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| | - Accuracy: 0.9905
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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: 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: 3
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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 |
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| | |:-------------:|:-----:|:----:|:---------------:|:--------:|
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| | | 0.2402 | 0.04 | 100 | 0.1240 | 0.961 |
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| | | 0.1607 | 0.09 | 200 | 0.1677 | 0.9765 |
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| | | 0.1589 | 0.13 | 300 | 0.0773 | 0.983 |
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| | | 0.0966 | 0.18 | 400 | 0.0812 | 0.986 |
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| | | 0.0916 | 0.22 | 500 | 0.0965 | 0.9835 |
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| | | 0.0922 | 0.27 | 600 | 0.0745 | 0.986 |
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| | | 0.0338 | 0.31 | 700 | 0.0745 | 0.9895 |
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| | | 0.1024 | 0.36 | 800 | 0.0807 | 0.9835 |
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| | | 0.0984 | 0.4 | 900 | 0.1363 | 0.9805 |
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| | | 0.0644 | 0.44 | 1000 | 0.0505 | 0.9905 |
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| | | 0.0582 | 0.49 | 1100 | 0.1026 | 0.9835 |
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| | | 0.038 | 0.53 | 1200 | 0.0535 | 0.992 |
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| | | 0.0554 | 0.58 | 1300 | 0.0566 | 0.991 |
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| |
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| |
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| | ### Framework versions
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| |
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| | - Transformers 4.36.2
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| | - Pytorch 2.1.1+cu118
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| | - Datasets 2.16.0
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| | - Tokenizers 0.15.0
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| | |
