Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Paper • 2202.06671 • Published • 2
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("malteos/specter-wol")
model = AutoModel.from_pretrained("malteos/specter-wol")Quick Links
Replicated SPECTER model based on w/o leakage training corpus with seed=0. See Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/specter-wol")