Instructions to use Cournane/roberta-base-reduced-Socks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Socks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Socks")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Socks") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Socks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cournane/roberta-base-reduced-Socks: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-reduced-Socks/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-reduced-Socks/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-reduced-Socks/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- cfbe59823d89eb98991bb37c2ea24ef9bab964d22e5c56d3c6b415cdbb6950d5
- Size of remote file:
- 499 MB
- SHA256:
- 06997435ad68a3562de045ed4a8f2ae305e4a8d0f2318f74cfb659fc59c804be
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