Instructions to use relbert/relbert-roberta-base-nce-semeval2012-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-roberta-base-nce-semeval2012-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-roberta-base-nce-semeval2012-2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-2") model = AutoModel.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from relbert/relbert-roberta-base-nce-semeval2012-2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://relbert/relbert-roberta-base-nce-semeval2012-2@refs/pr/1/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-2/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- b28223f3545c94b86915950d091b8342fd0214ae244d1ae6fa4b590888a2d3b1
- Size of remote file:
- 499 MB
- SHA256:
- b4c70aba115b9aea37f6758c47c99150b652bf727eee744bc8147b8054e24ae1
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