| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: albert-base-v2-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. --> | |
| # albert-base-v2-finetuned-code-mixed-DS | |
| This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/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 | |