---
library_name: transformers
license: mit
base_model: ai4bharat/IndicBERTv2-MLM-only
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# indic-bert-v2-mlm-only-dra-tam-mal-aw-classification-lora-r12

This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-only](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-only) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5211
- Accuracy: 0.7539
- F1: 0.7405

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.6914        | 0.2222 | 20   | 0.6860          | 0.5827   | 0.2928 |
| 0.6859        | 0.4444 | 40   | 0.6863          | 0.5338   | 0.6567 |
| 0.6855        | 0.6667 | 60   | 0.6765          | 0.6324   | 0.5248 |
| 0.6765        | 0.8889 | 80   | 0.6769          | 0.5542   | 0.6638 |
| 0.6689        | 1.1111 | 100  | 0.6595          | 0.6487   | 0.5174 |
| 0.6583        | 1.3333 | 120  | 0.6462          | 0.7001   | 0.6599 |
| 0.6456        | 1.5556 | 140  | 0.6297          | 0.6805   | 0.6942 |
| 0.626         | 1.7778 | 160  | 0.6033          | 0.7017   | 0.6995 |
| 0.6082        | 2.0    | 180  | 0.5898          | 0.7033   | 0.7065 |
| 0.5779        | 2.2222 | 200  | 0.5683          | 0.7188   | 0.6917 |
| 0.5886        | 2.4444 | 220  | 0.5554          | 0.7229   | 0.6909 |
| 0.5909        | 2.6667 | 240  | 0.5488          | 0.7311   | 0.7170 |
| 0.5607        | 2.8889 | 260  | 0.5435          | 0.7327   | 0.7244 |
| 0.5611        | 3.1111 | 280  | 0.5403          | 0.7368   | 0.7169 |
| 0.5375        | 3.3333 | 300  | 0.5375          | 0.7311   | 0.7140 |
| 0.5563        | 3.5556 | 320  | 0.5377          | 0.7425   | 0.7308 |
| 0.5562        | 3.7778 | 340  | 0.5340          | 0.7376   | 0.7130 |
| 0.568         | 4.0    | 360  | 0.5320          | 0.7457   | 0.7455 |
| 0.5598        | 4.2222 | 380  | 0.5265          | 0.7433   | 0.7185 |
| 0.5372        | 4.4444 | 400  | 0.5241          | 0.7531   | 0.7452 |
| 0.5366        | 4.6667 | 420  | 0.5344          | 0.7498   | 0.7542 |
| 0.5526        | 4.8889 | 440  | 0.5224          | 0.7514   | 0.7355 |
| 0.523         | 5.1111 | 460  | 0.5237          | 0.7482   | 0.7258 |
| 0.5362        | 5.3333 | 480  | 0.5235          | 0.7482   | 0.7406 |
| 0.5374        | 5.5556 | 500  | 0.5216          | 0.7514   | 0.7359 |
| 0.5621        | 5.7778 | 520  | 0.5213          | 0.7506   | 0.7325 |
| 0.5373        | 6.0    | 540  | 0.5211          | 0.7539   | 0.7405 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3