Text Classification
Transformers
LiteRT
ONNX
Safetensors
bert
language
detection
classification
text-embeddings-inference
Instructions to use dewdev/language_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/language_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dewdev/language_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dewdev/language_detection") model = AutoModelForSequenceClassification.from_pretrained("dewdev/language_detection") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-classification
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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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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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