| | ---
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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: super_clean_model
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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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| | # super_clean_model
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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: 0.2385
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| | - Accuracy: 0.9485
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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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| | | 0.6712 | 0.04 | 100 | 0.7864 | 0.6115 |
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| | | 0.4785 | 0.09 | 200 | 1.1828 | 0.7385 |
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| | | 0.475 | 0.13 | 300 | 0.3719 | 0.888 |
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| | | 0.3643 | 0.18 | 400 | 0.6170 | 0.887 |
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| | | 0.3546 | 0.22 | 500 | 0.6397 | 0.9045 |
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| | | 0.3796 | 0.27 | 600 | 0.2512 | 0.9435 |
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| | | 0.3301 | 0.31 | 700 | 0.2626 | 0.9135 |
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| | | 0.3343 | 0.36 | 800 | 0.4675 | 0.8745 |
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| | | 0.3578 | 0.4 | 900 | 0.2701 | 0.903 |
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| | | 0.2604 | 0.44 | 1000 | 0.2385 | 0.9485 |
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| | | 0.3236 | 0.49 | 1100 | 1.9438 | 0.6565 |
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| | | 0.3147 | 0.53 | 1200 | 1.2576 | 0.779 |
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| | | 0.2758 | 0.58 | 1300 | 0.3486 | 0.9345 |
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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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| |
|
