distilbert-base-multilingual-cased-language_detection
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0595
- Accuracy: 0.9971
- F1
- Weighted: 0.9971
- Micro: 0.9971
- Macro: 0.9977
- Recall
- Weighted: 0.9971
- Micro: 0.9971
- Macro: 0.9974
- Precision
- Weighted: 0.9971
- Micro: 0.9971
- Macro: 0.9981
Model description
This is a classification model of 16 different languages.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Language%20Detection/Language%20Detection-%2010k%20Samples/language_detection-10k.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/basilb2s/language-detection
Input Word Length:
Input Word Length By Class:
Class Distribution:
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0783 | 1.0 | 128 | 0.1544 | 0.9823 | 0.9819 | 0.9823 | 0.9806 | 0.9823 | 0.9823 | 0.9798 | 0.9847 | 0.9823 | 0.9852 |
| 0.1189 | 2.0 | 256 | 0.0595 | 0.9971 | 0.9971 | 0.9971 | 0.9977 | 0.9971 | 0.9971 | 0.9974 | 0.9971 | 0.9971 | 0.9981 |
| 0.0651 | 3.0 | 384 | 0.0473 | 0.9971 | 0.9971 | 0.9971 | 0.9977 | 0.9971 | 0.9971 | 0.9974 | 0.9971 | 0.9971 | 0.9981 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
