Instructions to use valurank/distilroberta-current with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valurank/distilroberta-current with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="valurank/distilroberta-current")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("valurank/distilroberta-current") model = AutoModelForSequenceClassification.from_pretrained("valurank/distilroberta-current", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from valurank/distilroberta-current: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/valurank/distilroberta-current/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://valurank/distilroberta-current/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/valurank/distilroberta-current/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- a5e57b84800c2134363c8debfa35b3a8d6ddfd28c38e1ac8c0c6743f29bdf80a
- Size of remote file:
- 329 MB
- SHA256:
- 5f0f29e1621f3ce1514dfe44e104601d990b4cb650c12d23135e9351377604c9
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