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Misinformation-Covid-Articles
Ghunghru/Misinformation-Covid-Articles
Misinformation-Covid-Articles is a text classification model from Ghunghru. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jan 9, 2024
- Updated
- Jan 9, 2024
- SHA
- 592b5f5b
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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. -->
Misinformation-Covid-Articles
This model is a fine-tuned version of bert-base-german-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1395
- Accuracy: 0.9840
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1266 | 1.0 | 3609 | 0.1224 | 0.9703 |
| 0.0639 | 2.0 | 7218 | 0.1170 | 0.9735 |
| 0.0437 | 3.0 | 10827 | 0.1230 | 0.9808 |
| 0.0148 | 4.0 | 14436 | 0.1209 | 0.9832 |
| 0.0044 | 5.0 | 18045 | 0.1395 | 0.9840 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jan 9, 2024
- Updated
- Jan 9, 2024