---
library_name: transformers
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-bert-hinglish-binary
  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-hinglish-binary

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: 0.7521
- Accuracy: 0.6681
- Precision: 0.6338
- Recall: 0.6182
- F1: 0.6213

## 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6539        | 0.9709 | 25   | 0.6510          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.6235        | 1.9806 | 51   | 0.6296          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.63          | 2.9903 | 77   | 0.6362          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.6149        | 4.0    | 103  | 0.6486          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.6088        | 4.9709 | 128  | 0.6229          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.5572        | 5.9806 | 154  | 0.6243          | 0.6376   | 0.3188    | 0.5    | 0.3894 |
| 0.4985        | 6.9903 | 180  | 0.6328          | 0.6322   | 0.3178    | 0.4957 | 0.3873 |
| 0.4697        | 8.0    | 206  | 0.6893          | 0.6730   | 0.6504    | 0.5829 | 0.5710 |
| 0.4114        | 8.9709 | 231  | 0.6825          | 0.6839   | 0.6531    | 0.6288 | 0.6327 |
| 0.3981        | 9.7087 | 250  | 0.6905          | 0.6866   | 0.6582    | 0.6228 | 0.6258 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0