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---
library_name: transformers
license: mit
base_model: ai4bharat/indic-bert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-bert-roman-urdu-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-roman-urdu-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: 0.5183
- Accuracy: 0.8847
- Precision: 0.8851
- Recall: 0.8831
- F1: 0.8839
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6103 | 0.9912 | 56 | 0.5319 | 0.7366 | 0.7534 | 0.7445 | 0.7355 |
| 0.3576 | 2.0 | 113 | 0.3626 | 0.8427 | 0.8418 | 0.8428 | 0.8422 |
| 0.2913 | 2.9912 | 169 | 0.3478 | 0.8589 | 0.8582 | 0.8585 | 0.8583 |
| 0.2351 | 4.0 | 226 | 0.3812 | 0.8564 | 0.8755 | 0.8486 | 0.8520 |
| 0.1342 | 4.9912 | 282 | 0.4025 | 0.8652 | 0.8678 | 0.8619 | 0.8636 |
| 0.0733 | 6.0 | 339 | 0.4448 | 0.8639 | 0.8638 | 0.8625 | 0.8630 |
| 0.0325 | 6.9912 | 395 | 0.5974 | 0.8589 | 0.8657 | 0.8540 | 0.8565 |
| 0.0308 | 8.0 | 452 | 0.6238 | 0.8589 | 0.8588 | 0.8575 | 0.8580 |
| 0.01 | 8.9912 | 508 | 0.6391 | 0.8664 | 0.8693 | 0.8631 | 0.8649 |
| 0.0091 | 9.9115 | 560 | 0.6417 | 0.8552 | 0.8548 | 0.8540 | 0.8543 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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