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xlm-roberta-large-finetuned-code-mixed-DS
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7328
- Accuracy: 0.7022
- Precision: 0.6437
- Recall: 0.6634
- F1: 0.6483
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: 8
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
1.098 | 0.5 | 248 | 1.0944 | 0.5352 | 0.2355 | 0.3344 | 0.2397 |
1.0827 | 1.0 | 496 | 1.0957 | 0.5352 | 0.5789 | 0.3379 | 0.2502 |
1.0503 | 1.5 | 744 | 0.9969 | 0.5312 | 0.3621 | 0.4996 | 0.3914 |
0.9728 | 2.0 | 992 | 0.8525 | 0.6056 | 0.5096 | 0.5565 | 0.4678 |
0.9271 | 2.49 | 1240 | 0.7809 | 0.6378 | 0.6014 | 0.6320 | 0.5963 |
0.7977 | 2.99 | 1488 | 0.8290 | 0.5875 | 0.5630 | 0.5918 | 0.5390 |
0.752 | 3.49 | 1736 | 0.7684 | 0.7123 | 0.6526 | 0.6610 | 0.6558 |
0.6846 | 3.99 | 1984 | 0.7328 | 0.7022 | 0.6437 | 0.6634 | 0.6483 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1