---
library_name: transformers
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: MahaPhrase_IndicBERT_Finetune_2
  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. -->

# MahaPhrase_IndicBERT_Finetune_2

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: 2.1777
- Accuracy: 0.792
- F1: 0.7907

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 282  | 0.5638          | 0.732    | 0.7293 |
| 0.5176        | 2.0   | 564  | 0.6016          | 0.732    | 0.7320 |
| 0.5176        | 3.0   | 846  | 0.6152          | 0.752    | 0.752  |
| 0.4173        | 4.0   | 1128 | 0.9357          | 0.732    | 0.7288 |
| 0.4173        | 5.0   | 1410 | 0.6378          | 0.744    | 0.7333 |
| 0.4395        | 6.0   | 1692 | 0.6688          | 0.768    | 0.7668 |
| 0.4395        | 7.0   | 1974 | 0.9093          | 0.764    | 0.7620 |
| 0.321         | 8.0   | 2256 | 0.9201          | 0.748    | 0.7468 |
| 0.2227        | 9.0   | 2538 | 0.7661          | 0.796    | 0.7909 |
| 0.2227        | 10.0  | 2820 | 1.3013          | 0.776    | 0.7613 |
| 0.1222        | 11.0  | 3102 | 1.2891          | 0.788    | 0.7870 |
| 0.1222        | 12.0  | 3384 | 1.3665          | 0.828    | 0.8257 |
| 0.0602        | 13.0  | 3666 | 1.4113          | 0.832    | 0.8307 |
| 0.0602        | 14.0  | 3948 | 1.8551          | 0.784    | 0.784  |
| 0.0222        | 15.0  | 4230 | 1.6908          | 0.788    | 0.7842 |
| 0.0134        | 16.0  | 4512 | 1.8649          | 0.788    | 0.7868 |
| 0.0134        | 17.0  | 4794 | 1.8400          | 0.796    | 0.7951 |
| 0.0022        | 18.0  | 5076 | 1.7347          | 0.8      | 0.7995 |
| 0.0022        | 19.0  | 5358 | 1.7748          | 0.796    | 0.7932 |
| 0.0039        | 20.0  | 5640 | 2.0701          | 0.808    | 0.8072 |
| 0.0039        | 21.0  | 5922 | 2.1207          | 0.808    | 0.8072 |
| 0.0008        | 22.0  | 6204 | 2.1592          | 0.8      | 0.7990 |
| 0.0008        | 23.0  | 6486 | 2.1735          | 0.796    | 0.7948 |
| 0.0           | 24.0  | 6768 | 2.1772          | 0.796    | 0.7948 |
| 0.0           | 25.0  | 7050 | 2.1777          | 0.792    | 0.7907 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0