Instructions to use danitamayo/roberta_language_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danitamayo/roberta_language_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="danitamayo/roberta_language_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("danitamayo/roberta_language_detection") model = AutoModelForSequenceClassification.from_pretrained("danitamayo/roberta_language_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from danitamayo/roberta_language_detection: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/danitamayo/roberta_language_detection/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://danitamayo/roberta_language_detection@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/danitamayo/roberta_language_detection/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- daa091c8e5762807035867f0bd3c1d7620c175d21571c2785cd59d708ad9cb3a
- Size of remote file:
- 17.1 MB
- SHA256:
- 46afe88da5fd71bdbab5cfab5e84c1adce59c246ea5f9341bbecef061891d0a7
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