Instructions to use relbert/relbert-roberta-base-nce-semeval2012-average 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-average 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-average")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-average") model = AutoModel.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-average", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from relbert/relbert-roberta-base-nce-semeval2012-average: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-average/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://relbert/relbert-roberta-base-nce-semeval2012-average@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-average/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 753c4abd9353a24739f5290d51b73e54a4410727f97d3f50a4f7d39e3543006f
- Size of remote file:
- 499 MB
- SHA256:
- 7efa8beef13f77d5eb16cd7e31d802db97eab142ecbed16d149ca6d9729ead60
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.