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malayalam-bert-FakeNews-Dravidian

Text Classification
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bert
Generated from Trainer
text-embeddings-inference
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  1. README.md +76 -0
  2. model.safetensors +1 -1
  3. runs/Nov22_15-42-54_e52886de8137/events.out.tfevents.1700667788.e52886de8137.80.0 +2 -2
README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ base_model: l3cube-pune/malayalam-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: malayalam-bert-FakeNews-Dravidian
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+ results: []
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+ ---
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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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+
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+ # malayalam-bert-FakeNews-Dravidian
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+
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+ This model is a fine-tuned version of [l3cube-pune/malayalam-bert](https://huggingface.co/l3cube-pune/malayalam-bert) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7928
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+ - Accuracy: 0.7840
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+ - Weighted f1 score: 0.7819
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+ - Macro f1 score: 0.7818
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 15
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+
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+ ### Training results
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+
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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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+ | 1.0681 | 1.0 | 204 | 1.0238 | 0.4982 | 0.3313 | 0.3325 |
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+ | 1.0077 | 2.0 | 408 | 0.9829 | 0.5031 | 0.3421 | 0.3433 |
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+ | 0.9738 | 3.0 | 612 | 0.9529 | 0.5877 | 0.5499 | 0.5503 |
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+ | 0.946 | 4.0 | 816 | 0.9267 | 0.6466 | 0.6401 | 0.6399 |
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+ | 0.9204 | 5.0 | 1020 | 0.9019 | 0.7006 | 0.6877 | 0.6875 |
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+ | 0.8961 | 6.0 | 1224 | 0.8754 | 0.7644 | 0.7629 | 0.7628 |
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+ | 0.8715 | 7.0 | 1428 | 0.8540 | 0.7607 | 0.7544 | 0.7543 |
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+ | 0.8485 | 8.0 | 1632 | 0.8362 | 0.7828 | 0.7789 | 0.7788 |
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+ | 0.8323 | 9.0 | 1836 | 0.8244 | 0.7791 | 0.7749 | 0.7748 |
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+ | 0.8182 | 10.0 | 2040 | 0.8151 | 0.7816 | 0.7773 | 0.7772 |
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+ | 0.8063 | 11.0 | 2244 | 0.8069 | 0.7816 | 0.7792 | 0.7791 |
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+ | 0.7973 | 12.0 | 2448 | 0.8011 | 0.7828 | 0.7799 | 0.7798 |
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+ | 0.791 | 13.0 | 2652 | 0.7950 | 0.7853 | 0.7840 | 0.7839 |
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+ | 0.7857 | 14.0 | 2856 | 0.7939 | 0.7816 | 0.7793 | 0.7792 |
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+ | 0.7826 | 15.0 | 3060 | 0.7928 | 0.7840 | 0.7819 | 0.7818 |
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+
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+
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+ ### Framework versions
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+
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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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