Instructions to use ERCDiDip/langdetect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ERCDiDip/langdetect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ERCDiDip/langdetect")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ERCDiDip/langdetect") model = AutoModelForSequenceClassification.from_pretrained("ERCDiDip/langdetect") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8aac3f79ad4a92703366526db6b02cb66867e7ab3cd1a3eea2cd811148d3367
- Size of remote file:
- 1.11 GB
- SHA256:
- aa7988cd6284d88a3def127de9855e97c4aa0782ca6d8f12582db9574e0476c8
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