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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indic-bert-finetuned-code-mixed-DS
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-finetuned-code-mixed-DS
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co./ai4bharat/indic-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8647
- Accuracy: 0.5795
- Precision: 0.5485
- Recall: 0.5287
- F1: 0.4391
## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0937 | 2.0 | 497 | 1.0813 | 0.3602 | 0.3587 | 0.4257 | 0.2834 |
| 1.0189 | 3.99 | 994 | 0.9482 | 0.5493 | 0.3887 | 0.5246 | 0.4080 |
| 0.9208 | 5.99 | 1491 | 0.9002 | 0.5714 | 0.3813 | 0.5292 | 0.4170 |
| 0.8803 | 7.98 | 1988 | 0.8758 | 0.5654 | 0.3889 | 0.5300 | 0.4159 |
| 0.8482 | 9.98 | 2485 | 0.8657 | 0.5795 | 0.3867 | 0.5365 | 0.4228 |
| 0.8293 | 11.98 | 2982 | 0.8734 | 0.5835 | 0.3796 | 0.5298 | 0.4214 |
| 0.8131 | 13.97 | 3479 | 0.8567 | 0.5835 | 0.5018 | 0.5414 | 0.4350 |
| 0.8 | 15.97 | 3976 | 0.8547 | 0.5835 | 0.5610 | 0.5460 | 0.4361 |
| 0.7933 | 17.96 | 4473 | 0.8650 | 0.5775 | 0.5317 | 0.5252 | 0.4373 |
| 0.7835 | 19.96 | 4970 | 0.8647 | 0.5795 | 0.5485 | 0.5287 | 0.4391 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
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