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FakeNews-bert-large-cased-stable
Denyol/FakeNews-bert-large-cased-stable
FakeNews-bert-large-cased-stable is a text classification model from Denyol. 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
- Nov 12, 2023
- Updated
- Nov 12, 2023
- SHA
- b4be2d8d
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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. -->
FakeNews-bert-large-cased-stable
This model is a fine-tuned version of bert-large-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1020
- Accuracy: 0.9827
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3549 | 1.0 | 802 | 0.3255 | 0.9453 |
| 0.1063 | 2.0 | 1605 | 0.1305 | 0.9771 |
| 0.0412 | 3.0 | 2407 | 0.1020 | 0.9827 |
| 0.0096 | 4.0 | 3210 | 0.1242 | 0.9822 |
| 0.0001 | 5.0 | 4010 | 0.1315 | 0.9827 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 12, 2023
- Updated
- Nov 12, 2023