Instructions to use apugachev/bert-langdetect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apugachev/bert-langdetect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apugachev/bert-langdetect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("apugachev/bert-langdetect") model = AutoModelForSequenceClassification.from_pretrained("apugachev/bert-langdetect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e068dbf7c3b8530fd6517a2507d5eafdf6fe6641196ab4df4b9146e85c4df721
- Size of remote file:
- 358 MB
- SHA256:
- 7fce7c1c513db40c538ee893380dc98642b3aed45eb8da1fb6c6d40c29fa931a
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