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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dce81351d7a5b4d6da81eb521fd7e85e46f3a2e52aef20aa867e8c521eee2115
- Size of remote file:
- 24.7 MB
- SHA256:
- e4a5bb93e2565e91c0edbea46dc65ac3de0e8d4fc730115f78776dc7c68d6dfd
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