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--- |
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library_name: transformers |
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tags: |
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- language |
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- detection |
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- classification |
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license: mit |
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datasets: |
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- hac541309/open-lid-dataset |
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pipeline_tag: text-classification |
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--- |
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This is a clone of https://huggingface.co/alexneakameni/language_detection with onnx format |
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# Language Detection Model |
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A **BERT-based** language detection model trained on [hac541309/open-lid-dataset](https://huggingface.co/datasets/hac541309/open-lid-dataset), which includes **121 million sentences across 200 languages**. This model is optimized for **fast and accurate** language identification in text classification tasks. |
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## Model Details |
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- **Architecture**: [BertForSequenceClassification](https://huggingface.co/transformers/model_doc/bert.html) |
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- **Hidden Size**: 384 |
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- **Number of Layers**: 4 |
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- **Attention Heads**: 6 |
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- **Max Sequence Length**: 512 |
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- **Dropout**: 0.1 |
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- **Vocabulary Size**: 50,257 |
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## Training Process |
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- **Dataset**: |
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- Used the [open-lid-dataset](https://huggingface.co/datasets/hac541309/open-lid-dataset) |
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- Split into train (90%) and test (10%) |
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- **Tokenizer**: A custom `BertTokenizerFast` with special tokens for `[UNK]`, `[CLS]`, `[SEP]`, `[PAD]`, `[MASK]` |
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- **Hyperparameters**: |
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- Learning Rate: 2e-5 |
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- Batch Size: 256 (training) / 512 (testing) |
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- Epochs: 1 |
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- Scheduler: Cosine |
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- **Trainer**: Leveraged the Hugging Face [Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer) with Weights & Biases for logging |
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## Evaluation |
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The model was evaluated on the test split. Below are the overall metrics: |
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- **Accuracy**: 0.969466 |
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- **Precision**: 0.969586 |
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- **Recall**: 0.969466 |
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- **F1 Score**: 0.969417 |
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Detailled evaluation (Size is the number of languages supported) |
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| Script | Support | Precision | Recall | F1 Score | Size | |
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|--------|---------|-----------|--------|----------|------| |
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| Arab | 819219 | 0.9038 | 0.9014 | 0.9023 | 21 | |
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| Latn | 7924704 | 0.9678 | 0.9663 | 0.9670 | 125 | |
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| Ethi | 144403 | 0.9967 | 0.9964 | 0.9966 | 2 | |
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| Beng | 163983 | 0.9949 | 0.9935 | 0.9942 | 3 | |
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| Deva | 423895 | 0.9495 | 0.9326 | 0.9405 | 10 | |
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| Cyrl | 831949 | 0.9899 | 0.9883 | 0.9891 | 12 | |
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| Tibt | 35683 | 0.9925 | 0.9930 | 0.9927 | 2 | |
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| Grek | 131155 | 0.9984 | 0.9990 | 0.9987 | 1 | |
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| Gujr | 86912 | 0.99999 | 0.9999 | 0.99995 | 1 | |
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| Hebr | 100530 | 0.9966 | 0.9995 | 0.9981 | 2 | |
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| Armn | 67203 | 0.9999 | 0.9998 | 0.9998 | 1 | |
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| Jpan | 88004 | 0.9983 | 0.9987 | 0.9985 | 1 | |
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| Knda | 67170 | 0.9999 | 0.9998 | 0.9999 | 1 | |
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| Geor | 70769 | 0.99997 | 0.9998 | 0.9999 | 1 | |
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| Khmr | 39708 | 1.0000 | 0.9997 | 0.9999 | 1 | |
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| Hang | 108509 | 0.9997 | 0.9999 | 0.9998 | 1 | |
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| Laoo | 29389 | 0.9999 | 0.9999 | 0.9999 | 1 | |
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| Mlym | 68418 | 0.99996 | 0.9999 | 0.9999 | 1 | |
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| Mymr | 100857 | 0.9999 | 0.9992 | 0.9995 | 2 | |
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| Orya | 44976 | 0.9995 | 0.9998 | 0.9996 | 1 | |
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| Guru | 67106 | 0.99999 | 0.9999 | 0.9999 | 1 | |
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| Olck | 22279 | 1.0000 | 0.9991 | 0.9995 | 1 | |
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| Sinh | 67492 | 1.0000 | 0.9998 | 0.9999 | 1 | |
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| Taml | 76373 | 0.99997 | 0.9999 | 0.9999 | 1 | |
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| Tfng | 41325 | 0.8512 | 0.8246 | 0.8247 | 2 | |
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| Telu | 62387 | 0.99997 | 0.9999 | 0.9999 | 1 | |
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| Thai | 83820 | 0.99995 | 0.9998 | 0.9999 | 1 | |
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| Hant | 152723 | 0.9945 | 0.9954 | 0.9949 | 2 | |
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| Hans | 92689 | 0.9893 | 0.9870 | 0.9882 | 1 | |
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A detailed per-script classification report is also provided in the repository for further analysis. |
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--- |
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### How to Use |
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You can quickly load and run inference with this model using the [Transformers pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines): |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("alexneakameni/language_detection") |
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model = AutoModelForSequenceClassification.from_pretrained("alexneakameni/language_detection") |
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language_detection = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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text = "Hello world!" |
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predictions = language_detection(text) |
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print(predictions) |
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``` |
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This will output the predicted language code or label with the corresponding confidence score. |
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--- |
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**Note**: The model’s performance may vary depending on text length, language variety, and domain-specific vocabulary. Always validate results against your own datasets for critical applications. |
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For more information, see the [repository documentation](https://github.com/KameniAlexNea/learning_language). |
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Thank you for using this model—feedback and contributions are welcome! |
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