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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/roberta-base |
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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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- accuracy |
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model-index: |
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- name: roberta-base-tqacd |
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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-base-tqacd |
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7467 |
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- F1 Macro: 0.2528 |
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- Precision: 0.2683 |
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- Recall: 0.2648 |
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- Accuracy: 0.3515 |
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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: 32 |
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- seed: 42 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Precision | Recall | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 114 | 2.3961 | 0.0436 | 0.0603 | 0.1103 | 0.1584 | |
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| No log | 2.0 | 228 | 2.3381 | 0.1234 | 0.1119 | 0.1476 | 0.3564 | |
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| No log | 3.0 | 342 | 2.1873 | 0.2052 | 0.3773 | 0.2457 | 0.2673 | |
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| No log | 4.0 | 456 | 2.2524 | 0.1800 | 0.2231 | 0.2043 | 0.3267 | |
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| 2.241 | 5.0 | 570 | 2.2141 | 0.2128 | 0.2340 | 0.2494 | 0.3218 | |
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| 2.241 | 6.0 | 684 | 2.3365 | 0.2238 | 0.2480 | 0.2391 | 0.2822 | |
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| 2.241 | 7.0 | 798 | 2.4779 | 0.2805 | 0.3501 | 0.2756 | 0.3713 | |
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| 2.241 | 8.0 | 912 | 2.5194 | 0.2518 | 0.2908 | 0.2667 | 0.3416 | |
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| 0.9793 | 9.0 | 1026 | 2.7467 | 0.2528 | 0.2683 | 0.2648 | 0.3515 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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