DunnBC22/distilbert-base-multilingual-cased-language_detection

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/NLPProjects/blob/main/Language%20Detection/Language%20Detection-%2010k%20Samples/languagedetection-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
• —trainbatchsize: 64
• —evalbatchsize: 64
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3

Training results

Framework versions

• —Transformers 4.26.1
• —Pytorch 1.12.1
• —Datasets 2.9.0
• —Tokenizers 0.12.1

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.