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---
base_model: google/muril-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: Muril-base-finetune-Tamil-qc
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. -->
# Muril-base-finetune-Tamil-qc
This model is a fine-tuned version of [google/muril-large-cased](https://huggingface.co/google/muril-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7585
- Precision: 0.8899
- Recall: 0.8887
- Accuracy: 0.8887
- F1-score: 0.8892
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | Accuracy | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.7778 | 1.0 | 155 | 0.4237 | 0.8573 | 0.8664 | 0.8664 | 0.8605 |
| 0.2769 | 2.0 | 310 | 0.3965 | 0.8789 | 0.8765 | 0.8765 | 0.8769 |
| 0.1657 | 3.0 | 465 | 0.4423 | 0.8889 | 0.8866 | 0.8866 | 0.8870 |
| 0.0975 | 4.0 | 620 | 0.5887 | 0.8824 | 0.8785 | 0.8785 | 0.8798 |
| 0.067 | 5.0 | 775 | 0.6212 | 0.8882 | 0.8846 | 0.8846 | 0.8858 |
| 0.034 | 6.0 | 930 | 0.6018 | 0.8948 | 0.8927 | 0.8927 | 0.8934 |
| 0.0249 | 7.0 | 1085 | 0.7035 | 0.8902 | 0.8887 | 0.8887 | 0.8893 |
| 0.0206 | 8.0 | 1240 | 0.7113 | 0.8936 | 0.8927 | 0.8927 | 0.8931 |
| 0.0122 | 9.0 | 1395 | 0.7400 | 0.8899 | 0.8887 | 0.8887 | 0.8892 |
| 0.0043 | 10.0 | 1550 | 0.7585 | 0.8899 | 0.8887 | 0.8887 | 0.8892 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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