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malayalam-bert-FakeNews-Dravidian
mdosama39/malayalam-bert-FakeNews-Dravidian
malayalam-bert-FakeNews-Dravidian is a text classification model from mdosama39. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- cc-by-4.0
- Model type
- bert
- Access
- Public
- Created
- Nov 22, 2023
- Updated
- Nov 22, 2023
- SHA
- 5d2bcf6f
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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. -->
malayalam-bert-FakeNews-Dravidian
This model is a fine-tuned version of l3cube-pune/malayalam-bert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7928
- Accuracy: 0.7840
- Weighted f1 score: 0.7819
- Macro f1 score: 0.7818
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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 score | Macro f1 score |
|---|---|---|---|---|---|---|
| 1.0681 | 1.0 | 204 | 1.0238 | 0.4982 | 0.3313 | 0.3325 |
| 1.0077 | 2.0 | 408 | 0.9829 | 0.5031 | 0.3421 | 0.3433 |
| 0.9738 | 3.0 | 612 | 0.9529 | 0.5877 | 0.5499 | 0.5503 |
| 0.946 | 4.0 | 816 | 0.9267 | 0.6466 | 0.6401 | 0.6399 |
| 0.9204 | 5.0 | 1020 | 0.9019 | 0.7006 | 0.6877 | 0.6875 |
| 0.8961 | 6.0 | 1224 | 0.8754 | 0.7644 | 0.7629 | 0.7628 |
| 0.8715 | 7.0 | 1428 | 0.8540 | 0.7607 | 0.7544 | 0.7543 |
| 0.8485 | 8.0 | 1632 | 0.8362 | 0.7828 | 0.7789 | 0.7788 |
| 0.8323 | 9.0 | 1836 | 0.8244 | 0.7791 | 0.7749 | 0.7748 |
| 0.8182 | 10.0 | 2040 | 0.8151 | 0.7816 | 0.7773 | 0.7772 |
| 0.8063 | 11.0 | 2244 | 0.8069 | 0.7816 | 0.7792 | 0.7791 |
| 0.7973 | 12.0 | 2448 | 0.8011 | 0.7828 | 0.7799 | 0.7798 |
| 0.791 | 13.0 | 2652 | 0.7950 | 0.7853 | 0.7840 | 0.7839 |
| 0.7857 | 14.0 | 2856 | 0.7939 | 0.7816 | 0.7793 | 0.7792 |
| 0.7826 | 15.0 | 3060 | 0.7928 | 0.7840 | 0.7819 | 0.7818 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- cc-by-4.0
- Created
- Nov 22, 2023
- Updated
- Nov 22, 2023