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human-vs-AI_bert-classifier
tomerz14/human-vs-AI_bert-classifier
human-vs-AI_bert-classifier is a text classification model from tomerz14. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 4, 2025
- Updated
- Oct 4, 2025
- SHA
- 226b20d9
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
human-vs-AI_bert-classifier
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0011
- Accuracy: 1.0
- F1: 1.0
- Roc Auc: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 14
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Roc Auc |
|---|---|---|---|---|---|---|
| 0.333 | 0.2475 | 50 | 0.0377 | 1.0 | 1.0 | 1.0 |
| 0.0108 | 0.4950 | 100 | 0.0023 | 1.0 | 1.0 | 1.0 |
| 0.0105 | 0.7426 | 150 | 0.0015 | 1.0 | 1.0 | 1.0 |
| 0.0104 | 0.9901 | 200 | 0.0011 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 4, 2025
- Updated
- Oct 4, 2025