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--- |
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license: apache-2.0 |
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base_model: google/electra-base-discriminator |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: trueparagraph.ai-ELECTRA |
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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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# trueparagraph.ai-ELECTRA |
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This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9430 |
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- F1: 0.9421 |
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- Precision: 0.9528 |
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- Recall: 0.9316 |
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- Mcc: 0.8862 |
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- Roc Auc: 0.9429 |
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- Pr Auc: 0.9217 |
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- Log Loss: 0.8825 |
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- Loss: 0.2952 |
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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: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Mcc | Roc Auc | Pr Auc | Log Loss | Validation Loss | |
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|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:------:|:-------:|:------:|:--------:|:---------------:| |
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| 0.5401 | 0.6297 | 500 | 0.7694 | 0.7044 | 0.9732 | 0.5519 | 0.5963 | 0.7684 | 0.7602 | 3.5789 | 0.6109 | |
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| 0.3122 | 1.2594 | 1000 | 0.9225 | 0.9231 | 0.9122 | 0.9342 | 0.8452 | 0.9225 | 0.8850 | 1.1485 | 0.2368 | |
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| 0.2301 | 1.8892 | 1500 | 0.8670 | 0.8811 | 0.7942 | 0.9892 | 0.7573 | 0.8676 | 0.7910 | 1.9476 | 0.3654 | |
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| 0.1608 | 2.5189 | 2000 | 0.9348 | 0.9364 | 0.9103 | 0.9639 | 0.8711 | 0.9349 | 0.8955 | 1.0090 | 0.2677 | |
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| 0.1146 | 3.1486 | 2500 | 0.9430 | 0.9421 | 0.9528 | 0.9316 | 0.8862 | 0.9429 | 0.9217 | 0.8825 | 0.2952 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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