SPECTER 2.0

SPECTER 2.0 is the successor to SPECTER and is capable of generating task specific embeddings for scientific tasks when paired with adapters.
Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.

Model Details

Model Description

SPECTER 2.0 has been trained on over 6M triplets of scientific paper citations, which are available here.
Post that it is trained on all the SciRepEval training tasks, with task format specific adapters.
Task Formats trained on:

Classification
Regression
Proximity
Adhoc Search

It builds on the work done in SciRepEval: A Multi-Format Benchmark for Scientific Document Representations and we evaluate the trained model on this benchmark as well.

Developed by: Amanpreet Singh, Mike D’Arcy, Arman Cohan, Doug Downey, Sergey Feldman
Shared by : Allen AI
Model type: bert-base-uncased + adapters
License: Apache 2.0
Finetuned from model: allenai/scibert.

Model Sources

Repository: https://github.com/allenai/SPECTER2_0
Paper: https://api.semanticscholar.org/CorpusID:254018137
Demo: Usage

Uses

Direct Use

Model	Type	Name and HF link
Base	Transformer	allenai/specter2
Classification	Adapter	allenai/specter2_classification
Regression	Adapter	allenai/specter2_regression
Retrieval	Adapter	allenai/specter2_proximity
Adhoc Query	Adapter	allenai/specter2_adhoc_query

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