---
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: indic-bert-MLTC-BB1
  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-MLTC-BB1

This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5041
- F1: 0.7518
- Roc Auc: 0.7539
- Accuracy: 0.3728
- Hamming Loss: 0.2461
- Jaccard Score: 0.6023
- Zero One Loss: 0.6272

## 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-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.6264        | 1.0   | 49   | 0.6551          | 0.6188 | 0.6176  | 0.1028   | 0.3824       | 0.4481        | 0.8972        |
| 0.6024        | 2.0   | 98   | 0.6163          | 0.6967 | 0.6442  | 0.3316   | 0.3554       | 0.5345        | 0.6684        |
| 0.5574        | 3.0   | 147  | 0.5932          | 0.7081 | 0.6492  | 0.3548   | 0.3503       | 0.5481        | 0.6452        |
| 0.5267        | 4.0   | 196  | 0.6041          | 0.7105 | 0.6512  | 0.3573   | 0.3483       | 0.5510        | 0.6427        |
| 0.4988        | 5.0   | 245  | 0.5409          | 0.7215 | 0.6822  | 0.3573   | 0.3175       | 0.5644        | 0.6427        |
| 0.4609        | 6.0   | 294  | 0.5189          | 0.7188 | 0.6880  | 0.3419   | 0.3117       | 0.5611        | 0.6581        |
| 0.4214        | 7.0   | 343  | 0.5426          | 0.7423 | 0.7196  | 0.3676   | 0.2802       | 0.5902        | 0.6324        |
| 0.426         | 8.0   | 392  | 0.5119          | 0.7478 | 0.7416  | 0.3702   | 0.2584       | 0.5972        | 0.6298        |
| 0.4034        | 9.0   | 441  | 0.5065          | 0.7526 | 0.7506  | 0.3805   | 0.2494       | 0.6033        | 0.6195        |
| 0.3974        | 10.0  | 490  | 0.5041          | 0.7518 | 0.7539  | 0.3728   | 0.2461       | 0.6023        | 0.6272        |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1