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albert-base-v2-finetuned-code-mixed-DS
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0408
- Accuracy: 0.7324
- Precision: 0.6883
- Recall: 0.6822
- F1: 0.6833
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: 1.2766380106570283e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9207 | 1.0 | 497 | 0.7810 | 0.6016 | 0.5878 | 0.5953 | 0.5264 |
| 0.7519 | 2.0 | 994 | 0.8159 | 0.6600 | 0.6020 | 0.6194 | 0.6015 |
| 0.6029 | 3.0 | 1491 | 0.8026 | 0.7163 | 0.6599 | 0.6604 | 0.6593 |
| 0.4259 | 4.0 | 1988 | 0.9464 | 0.7384 | 0.7058 | 0.6808 | 0.6822 |
| 0.2845 | 5.0 | 2485 | 1.0408 | 0.7324 | 0.6883 | 0.6822 | 0.6833 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
