Instructions to use relbert/relbert-albert-base-nce-semeval2012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-albert-base-nce-semeval2012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-albert-base-nce-semeval2012")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-albert-base-nce-semeval2012") model = AutoModel.from_pretrained("relbert/relbert-albert-base-nce-semeval2012", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d4a401b3884a9e319ad323a357e8fe366e599d1dec5ee45775bbc4929eea59f
- Size of remote file:
- 46.7 MB
- SHA256:
- 8596600a762e6a84965f856609f70aed864aa8230d809706466929a3bbf260cb
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