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--- |
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license: mit |
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base_model: ai4bharat/indic-bert |
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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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model-index: |
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- name: indic-bert-FakeNews-Dravidiant |
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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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# indic-bert-FakeNews-Dravidiant |
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This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6230 |
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- Accuracy: 0.6822 |
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- Weighted f1 score: 0.6816 |
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- Macro f1 score: 0.6816 |
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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: 2e-06 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 score | Macro f1 score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------:| |
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| 1.0142 | 1.0 | 204 | 0.7823 | 0.5509 | 0.5402 | 0.5405 | |
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| 0.7202 | 2.0 | 408 | 0.6967 | 0.5902 | 0.5747 | 0.5744 | |
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| 0.6758 | 3.0 | 612 | 0.6730 | 0.6319 | 0.6224 | 0.6221 | |
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| 0.6471 | 4.0 | 816 | 0.6547 | 0.6417 | 0.6336 | 0.6334 | |
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| 0.6221 | 5.0 | 1020 | 0.6396 | 0.6663 | 0.6646 | 0.6645 | |
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| 0.6005 | 6.0 | 1224 | 0.6322 | 0.6724 | 0.6723 | 0.6723 | |
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| 0.5801 | 7.0 | 1428 | 0.6385 | 0.6601 | 0.6530 | 0.6528 | |
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| 0.5613 | 8.0 | 1632 | 0.6249 | 0.6810 | 0.6797 | 0.6796 | |
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| 0.5534 | 9.0 | 1836 | 0.6231 | 0.6834 | 0.6831 | 0.6831 | |
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| 0.5428 | 10.0 | 2040 | 0.6230 | 0.6822 | 0.6816 | 0.6816 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.14.1 |
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