Instructions to use SparkBeyond/roberta-large-sts-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparkBeyond/roberta-large-sts-b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SparkBeyond/roberta-large-sts-b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SparkBeyond/roberta-large-sts-b") model = AutoModelForSequenceClassification.from_pretrained("SparkBeyond/roberta-large-sts-b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SparkBeyond/roberta-large-sts-b: direct link, hf CLI and curl.
- Browser
- Download file 1.43 GB
-
https://huggingface.co/SparkBeyond/roberta-large-sts-b/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SparkBeyond/roberta-large-sts-b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SparkBeyond/roberta-large-sts-b/resolve/main/pytorch_model.bin
1.43 GB
- Xet hash:
- a65a4bcfdd81358525a5247d8ec54cdd4836736f452c8575da8db56906595b1e
- Size of remote file:
- 1.43 GB
- SHA256:
- abdc058d925751a9f0cc6ad7a339c2a1f92a1c416f7f584dedd36ca8fe3968ea
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