SPECTER2

SPECTER2 is a family of models that succeeds 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.

Note:For general embedding purposes, please use allenai/specter2.

To get the best performance on a downstream task type please load the associated adapter () with the base model as in the example below.

Dec 2023 Update:

Model usage updated to be compatible with latest versions of transformers and adapters (newly released update to adapter-transformers) libraries.

******Update******

This update introduces a new set of SPECTER 2.0 models with the base transformer encoder pre-trained on an extended citation dataset containing more recent papers. For benchmarking purposes please use the existing SPECTER 2.0 models w/o the aug2023refresh suffix viz. allenai/specter2_base.

Adapter allenai/specter2_aug2023refresh_adhoc_query for allenai/specter2_aug2023refresh_base

An adapter for the None model that was trained on the allenai/scirepeval dataset.

This adapter was created for usage with the adapters library.

Usage

First, install adapters:

pip install -U adapters

Note: adapters is built as an add-on to transformers that acts as a drop-in replacement with adapter support. More

Now, the adapter can be loaded and activated like this:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("allenai/specter2_aug2023refresh_base")
adapter_name = model.load_adapter("allenai/specter2_aug2023refresh_adhoc_query", source="hf", set_active=True)

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 with additionally attached task format specific adapter modules on all the SciRepEval training tasks.

Task Formats trained on:

• Classification
• Regression
• Proximity
• Adhoc Search

This is the adhoc search query specific adapter. For tasks where papers have to retrieved for a short textual query, use this adapter to encode the query and allenai/specter2_proximity to encode the candidates.

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	Name and HF link	Description
Proximity*	allenai/specter2_aug2023refresh	Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search
Adhoc Query	allenai/specter2_aug2023refresh_adhoc_query	Encode short raw text queries for search tasks. (Candidate papers can be encoded with the proximity adapter)
Classification	allenai/specter2_aug2023refresh_classification	Encode papers to feed into linear classifiers as features
Regression	allenai/specter2_aug2023refresh_regression	Encode papers to feed into linear regressors as features

*Proximity model should suffice for downstream task types not mentioned above

from transformers import AutoTokenizer
from adapters import AutoAdapterModel
from sklearn.metrics.pairwise import euclidean_distances

def embed_input(text_batch: List[str]):
  # preprocess the input
  inputs = self.tokenizer(text_batch, padding=True, truncation=True,
                                   return_tensors="pt", return_token_type_ids=False, max_length=512)
  output = model(**inputs)
  # take the first token in the batch as the embedding
  embeddings = output.last_hidden_state[:, 0, :]
  return embeddings

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_aug2023refresh_base_base')

#load base model
model = AutoAdapterModel.from_pretrained('allenai/specter2_aug2023refresh_base_aug2023refresh_base_base')

#load the query adapter, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("allenai/specter2_aug2023refresh_base_adhoc_query", source="hf", load_as="specter2_adhoc_query", set_active=True)
query = ["Bidirectional transformers"]
query_embedding = embed_input(query)

#load the proximity adapter, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("allenai/specter2_aug2023refresh_base", source="hf", load_as="specter2_proximity", set_active=True)
papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
          {'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]
# concatenate title and abstract
text_papers_batch = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
paper_embeddings = embed_input(text_papers_batch)

#Calculate L2 distance between query and papers
l2_distance = euclidean_distances(papers, query).flatten()

Downstream Use

For evaluation and downstream usage, please refer to https://github.com/allenai/scirepeval/blob/main/evaluation/INFERENCE.md.

Training Details

Training Data

The base model is trained on citation links between papers and the adapters are trained on 8 large scale tasks across the four formats. All the data is a part of SciRepEval benchmark and is available here.

The citation link are triplets in the form

{"query": {"title": ..., "abstract": ...}, "pos": {"title": ..., "abstract": ...}, "neg": {"title": ..., "abstract": ...}}

consisting of a query paper, a positive citation and a negative which can be from the same/different field of study as the query or citation of a citation.

Training Procedure

Please refer to the SPECTER paper.

Training Hyperparameters

The model is trained in two stages using SciRepEval:

• Base Model: First a base model is trained on the above citation triplets.

batch size = 1024, max input length = 512, learning rate = 2e-5, epochs = 2 warmup steps = 10% fp16

• Adapters: Thereafter, task format specific adapters are trained on the SciRepEval training tasks, where 600K triplets are sampled from above and added to the training data as well.

batch size = 256, max input length = 512, learning rate = 1e-4, epochs = 6 warmup = 1000 steps fp16

Evaluation

We evaluate the model on SciRepEval, a large scale eval benchmark for scientific embedding tasks which which has [SciDocs] as a subset. We also evaluate and establish a new SoTA on MDCR, a large scale citation recommendation benchmark.

Model	SciRepEval In-Train	SciRepEval Out-of-Train	SciRepEval Avg	MDCR(MAP, Recall@5)
BM-25	n/a	n/a	n/a	(33.7, 28.5)
SPECTER	54.7	57.4	68.0	(30.6, 25.5)
SciNCL	55.6	57.8	69.0	(32.6, 27.3)
SciRepEval-Adapters	61.9	59.0	70.9	(35.3, 29.6)
SPECTER 2.0-Adapters	62.3	59.2	71.2	(38.4, 33.0)

Please cite the following works if you end up using SPECTER 2.0:

SPECTER paper:

@inproceedings{specter2020cohan,
  title={{SPECTER: Document-level Representation Learning using Citation-informed Transformers}},
  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
  booktitle={ACL},
  year={2020}
}

SciRepEval paper

@inproceedings{Singh2022SciRepEvalAM,
  title={SciRepEval: A Multi-Format Benchmark for Scientific Document Representations},
  author={Amanpreet Singh and Mike D'Arcy and Arman Cohan and Doug Downey and Sergey Feldman},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2022},
  url={https://api.semanticscholar.org/CorpusID:254018137}
}