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license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-non-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. -->
# roberta-large-finetuned-non-code-mixed-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1265
- Accuracy: 0.6936
- Precision: 0.6794
- Recall: 0.6782
- F1: 0.6784
## 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-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0688 | 1.0 | 463 | 0.8847 | 0.6127 | 0.6038 | 0.6032 | 0.6014 |
| 0.8226 | 2.0 | 926 | 0.7622 | 0.6796 | 0.6769 | 0.6822 | 0.6716 |
| 0.6844 | 2.99 | 1389 | 0.8391 | 0.6828 | 0.6718 | 0.6563 | 0.6602 |
| 0.536 | 3.99 | 1852 | 0.8218 | 0.6990 | 0.6950 | 0.6807 | 0.6844 |
| 0.3938 | 4.99 | 2315 | 0.9616 | 0.6958 | 0.6967 | 0.7056 | 0.6880 |
| 0.2674 | 5.99 | 2778 | 1.1389 | 0.7033 | 0.6868 | 0.6895 | 0.6879 |
| 0.2073 | 6.98 | 3241 | 1.5578 | 0.6915 | 0.6786 | 0.6807 | 0.6792 |
| 0.1641 | 7.98 | 3704 | 1.9538 | 0.6850 | 0.6734 | 0.6715 | 0.6717 |
| 0.1394 | 8.98 | 4167 | 2.3230 | 0.6893 | 0.6733 | 0.6742 | 0.6736 |
| 0.1248 | 9.98 | 4630 | 2.4050 | 0.6936 | 0.6824 | 0.6819 | 0.6815 |
| 0.1002 | 10.98 | 5093 | 2.4227 | 0.6947 | 0.6832 | 0.6932 | 0.6795 |
| 0.0776 | 11.97 | 5556 | 2.5782 | 0.7012 | 0.6876 | 0.6923 | 0.6887 |
| 0.0685 | 12.97 | 6019 | 2.7967 | 0.6915 | 0.6836 | 0.6930 | 0.6820 |
| 0.045 | 13.97 | 6482 | 2.8884 | 0.7044 | 0.6873 | 0.6855 | 0.6863 |
| 0.0462 | 14.97 | 6945 | 2.9528 | 0.6947 | 0.6754 | 0.6749 | 0.6751 |
| 0.0444 | 15.97 | 7408 | 3.0356 | 0.6904 | 0.6778 | 0.6805 | 0.6778 |
| 0.0343 | 16.96 | 7871 | 3.0123 | 0.6936 | 0.6784 | 0.6762 | 0.6771 |
| 0.0245 | 17.96 | 8334 | 3.0160 | 0.6893 | 0.6720 | 0.6735 | 0.6727 |
| 0.0198 | 18.96 | 8797 | 3.1597 | 0.6904 | 0.6741 | 0.6727 | 0.6732 |
| 0.0189 | 19.96 | 9260 | 3.1265 | 0.6936 | 0.6794 | 0.6782 | 0.6784 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
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