---
license: cc-by-4.0
base_model: l3cube-pune/malayalam-bert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: malayalam-bert-FakeNews-Dravidian
  results: []
---

<!-- 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](https://huggingface.co/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