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--- |
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library_name: transformers |
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license: mit |
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base_model: 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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment_model |
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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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# sentiment_model |
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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: 0.2038 |
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- Accuracy: 0.9782 |
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- Precision: 0.9791 |
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- Recall: 0.9782 |
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- F1: 0.9782 |
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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: 5e-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: Use OptimizerNames.ADAMW_TORCH 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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.2491 | 1.0 | 553 | 0.2143 | 0.95 | 0.9522 | 0.95 | 0.9499 | |
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| 0.1334 | 2.0 | 1106 | 0.1648 | 0.9679 | 0.9699 | 0.9679 | 0.9679 | |
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| 0.0002 | 3.0 | 1659 | 0.1815 | 0.9756 | 0.9768 | 0.9756 | 0.9756 | |
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| 0.0002 | 4.0 | 2212 | 0.2997 | 0.9615 | 0.9643 | 0.9615 | 0.9615 | |
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| 0.0001 | 5.0 | 2765 | 0.2159 | 0.9769 | 0.9779 | 0.9769 | 0.9769 | |
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| 0.0001 | 6.0 | 3318 | 0.2038 | 0.9782 | 0.9791 | 0.9782 | 0.9782 | |
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### Framework versions |
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- Transformers 4.52.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.1 |
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