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:
- 0e440e7a76442f43b2e29c7778363ddd342f0bca5418a44d2a37bb9bb34ee019
- Size of remote file:
- 97.9 MB
- SHA256:
- 461b6435494b6d38df94c6c01ed6f31325577407a0c5094bce7d33222bc3314b
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