---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-bert-finetuned-ours-DS
  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. -->

# indic-bert-finetuned-ours-DS

This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0941
- Accuracy: 0.275
- Precision: 0.3056
- Recall: 0.3467
- F1: 0.1803

## 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: 1e-07
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0988        | 0.99  | 99   | 1.0984          | 0.3      | 0.3611    | 0.3661 | 0.2750 |
| 1.0981        | 1.98  | 198  | 1.0980          | 0.29     | 0.2713    | 0.3568 | 0.1997 |
| 1.0981        | 2.97  | 297  | 1.0977          | 0.315    | 0.3029    | 0.3736 | 0.2259 |
| 1.0976        | 3.96  | 396  | 1.0974          | 0.3      | 0.2816    | 0.3601 | 0.2122 |
| 1.0976        | 4.95  | 495  | 1.0971          | 0.295    | 0.2780    | 0.3601 | 0.2041 |
| 1.097         | 5.94  | 594  | 1.0968          | 0.29     | 0.2680    | 0.3533 | 0.2012 |
| 1.0962        | 6.93  | 693  | 1.0965          | 0.3      | 0.2816    | 0.3601 | 0.2122 |
| 1.0963        | 7.92  | 792  | 1.0963          | 0.29     | 0.2761    | 0.3533 | 0.2012 |
| 1.0969        | 8.91  | 891  | 1.0961          | 0.3      | 0.2895    | 0.3601 | 0.2122 |
| 1.0958        | 9.9   | 990  | 1.0958          | 0.3      | 0.2895    | 0.3601 | 0.2122 |
| 1.0959        | 10.89 | 1089 | 1.0956          | 0.3      | 0.2983    | 0.3601 | 0.2122 |
| 1.0953        | 11.88 | 1188 | 1.0954          | 0.3      | 0.2983    | 0.3601 | 0.2122 |
| 1.0955        | 12.87 | 1287 | 1.0952          | 0.295    | 0.3019    | 0.3567 | 0.2067 |
| 1.0948        | 13.86 | 1386 | 1.0951          | 0.295    | 0.3083    | 0.3601 | 0.2040 |
| 1.095         | 14.85 | 1485 | 1.0949          | 0.29     | 0.3013    | 0.3568 | 0.1983 |
| 1.0951        | 15.84 | 1584 | 1.0948          | 0.29     | 0.3013    | 0.3568 | 0.1983 |
| 1.0948        | 16.83 | 1683 | 1.0946          | 0.29     | 0.3143    | 0.3568 | 0.1982 |
| 1.0942        | 17.82 | 1782 | 1.0945          | 0.29     | 0.3291    | 0.3568 | 0.1982 |
| 1.0949        | 18.81 | 1881 | 1.0944          | 0.28     | 0.3145    | 0.3500 | 0.1863 |
| 1.095         | 19.8  | 1980 | 1.0943          | 0.275    | 0.3056    | 0.3467 | 0.1803 |
| 1.0945        | 20.79 | 2079 | 1.0943          | 0.275    | 0.3056    | 0.3467 | 0.1803 |
| 1.0942        | 21.78 | 2178 | 1.0942          | 0.275    | 0.3056    | 0.3467 | 0.1803 |
| 1.0938        | 22.77 | 2277 | 1.0942          | 0.275    | 0.3056    | 0.3467 | 0.1803 |
| 1.0953        | 23.76 | 2376 | 1.0941          | 0.275    | 0.3056    | 0.3467 | 0.1803 |
| 1.0943        | 24.75 | 2475 | 1.0941          | 0.275    | 0.3056    | 0.3467 | 0.1803 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1