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--- |
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library_name: transformers |
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license: mit |
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base_model: 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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Gregariousness_binary |
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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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# Gregariousness_binary |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6050 |
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- Accuracy: 0.6794 |
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- Precision: 0.6869 |
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- Recall: 0.5980 |
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- F1: 0.6394 |
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- Auc: 0.6756 |
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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: 32 |
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- eval_batch_size: 32 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| No log | 1.0 | 134 | 0.6922 | 0.4753 | 0.4753 | 1.0 | 0.6443 | 0.5 | |
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| No log | 2.0 | 268 | 0.6288 | 0.6207 | 0.7696 | 0.2882 | 0.4194 | 0.6050 | |
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| No log | 3.0 | 402 | 0.6050 | 0.6794 | 0.6869 | 0.5980 | 0.6394 | 0.6756 | |
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### Framework versions |
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- Transformers 4.44.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.19.1 |
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