---
library_name: transformers
license: mit
base_model: ai4bharat/IndicBERTv2-MLM-Sam-TLM
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sutra-classifier-adv
  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. -->

# sutra-classifier-adv

This model is a fine-tuned version of [ai4bharat/IndicBERTv2-MLM-Sam-TLM](https://huggingface.co/ai4bharat/IndicBERTv2-MLM-Sam-TLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9104
- Accuracy: 0.9020
- F1: 0.9020
- Adv Acc: 0.0

## 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: 8.8e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.079905
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Adv Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|
| 0.8968        | 1.0   | 72   | 1.9034          | 0.8078   | 0.8048 | 0.0902  |
| 2.4258        | 2.0   | 144  | 5.0244          | 0.8510   | 0.8503 | 0.1333  |
| 6.9155        | 3.0   | 216  | 8.0457          | 0.8549   | 0.8542 | 0.0     |
| 7.4505        | 4.0   | 288  | 12.3074         | 0.8706   | 0.8706 | 0.0     |
| 10.0671       | 5.0   | 360  | 9.0296          | 0.8353   | 0.8334 | 0.0     |
| 8.9089        | 6.0   | 432  | 10.0976         | 0.8275   | 0.8274 | 0.0353  |
| 6.8576        | 7.0   | 504  | 6.8378          | 0.8824   | 0.8824 | 0.0118  |
| 5.9407        | 8.0   | 576  | 5.4805          | 0.8588   | 0.8583 | 0.0314  |
| 5.1865        | 9.0   | 648  | 4.0087          | 0.8745   | 0.8745 | 0.1412  |
| 3.5606        | 10.0  | 720  | 4.4264          | 0.8549   | 0.8546 | 0.0078  |
| 3.4944        | 11.0  | 792  | 3.9312          | 0.9098   | 0.9098 | 0.0     |
| 3.4093        | 12.0  | 864  | 4.0868          | 0.8902   | 0.8902 | 0.0     |
| 3.3966        | 13.0  | 936  | 3.9650          | 0.8980   | 0.8979 | 0.0     |
| 3.3191        | 14.0  | 1008 | 4.0058          | 0.8824   | 0.8821 | 0.0     |
| 3.3156        | 15.0  | 1080 | 4.0279          | 0.8784   | 0.8784 | 0.0     |
| 3.26          | 16.0  | 1152 | 4.0243          | 0.8902   | 0.8902 | 0.0     |
| 3.264         | 17.0  | 1224 | 3.9404          | 0.8941   | 0.8941 | 0.0     |
| 3.2533        | 18.0  | 1296 | 3.8867          | 0.8941   | 0.8941 | 0.0     |
| 3.2137        | 19.0  | 1368 | 3.8939          | 0.9020   | 0.9020 | 0.0     |
| 3.2148        | 20.0  | 1440 | 3.9104          | 0.9020   | 0.9020 | 0.0     |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4