SPECTER: Document-level Representation Learning using Citation-informed Transformers
Paper • 2004.07180 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allenai/specter")
model = AutoModel.from_pretrained("allenai/specter")Quick Links
SPECTER
SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning.
If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.
Paper: SPECTER: Document-level Representation Learning using Citation-informed Transformers
Original Repo: Github
Evaluation Benchmark: SciDocs
Authors: Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allenai/specter")