SPECTER2

Dec 2023 Update:

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

Aug 2023 Update:

1. The SPECTER2 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:

Old Name	New Name
allenai/specter2	allenai/specter2_base
allenai/specter2_proximity	allenai/specter2

2. We have a parallel version (termed aug2023refresh) where the base transformer encoder version is pre-trained on a collection of newer papers (published after 2018). However, for benchmarking purposes, please continue using the current version.

Overview

SPECTER2 is a collection of document embedding models for scientific tasks. It builds on the original SPECTER and SciRepEval works, and can be used to generate specific embeddings for multiple task formats i.e Classification, Regression, Retrieval and Search based on the chosen type of associated adapter (examples below).

Note: To get the best performance for a particular task format, please load the appropriate adapter along with the base transformer model as given below.

Setup

If using the existing model weights for inference:

https://github.com/allenai/SPECTER2.git
cd SPECTER2
conda create -n specter2 python=3.8
pip install -e .
pip install -r requirements.txt

For training/ benchmarking, please setup SciRepEval

Usage

We train a base model from scratch on citation links like SPECTER, but our training data consists of 6M (10x) triplets spanning 23 fields of studies. Then we train task format specific adapters with SciRepEval to generate multiple embeddings for the same paper. We represent the input paper as a concatenation of its title and abstract. For Search type tasks where the input query is a short text rather a paper, use the adhoc query model below to encode it and the retrieval model to encode the candidate papers.
All the models are publicly available on HuggingFace and AWS S3.

HuggingFace

Model	Name and HF link	Description
Retrieval*	allenai/specter2_proximity	Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search
Adhoc Query	allenai/specter2_adhoc_query	Encode short raw text queries for search tasks. (Candidate papers can be encoded with proximity)
Classification	allenai/specter2_classification	Encode papers to feed into linear classifiers as features
Regression	allenai/specter2_regression	Encode papers to feed into linear regressors as features

*Retrieval model should suffice for any other downstream task types not mentioned above

from transformers import AutoTokenizer
from adapters import AutoAdapterModel

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')

#load base model
model = AutoAdapterModel.from_pretrained('allenai/specter2_base')

#load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("allenai/specter2", source="hf", load_as="proximity", set_active=True)
#other possibilities: allenai/specter2_<classification|regression|adhoc_query>

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_batch = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# 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, :]

AWS S3 via CLI

mkdir -p specter2/models
cd specter2/models
aws s3 --no-sign-request cp s3://ai2-s2-research-public/specter2_0/specter2_0.tar.gz .
tar -xvf specter2_0.tar.gz

The above commands will copy all the model weights from S3 as a tar archive and extract two folders-base and adapters.

from transformers import AutoTokenizer
from adapters import AutoAdapterModel

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('specter2/models/base')

#load base model
model = AutoAdapterModel.from_pretrained('specter2/models/base')

#load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("specter2/models/adapters/proximity", load_as="proximity", set_active=True) 
#other possibilities: .../adapters/<classification|regression|adhoc_query>

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_batch = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# 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, :]

Batch Processing for multiple task types (requires GPU)

To generate the embeddings for an input batch, follow INFERENCE.md. Create the Model instance as follows:

adapters_dict = {"[CLF]": "allenai/specter2_classification", "[QRY]": "allenai/specter2_adhoc_query", "[RGN]": "allenai/specter2_regression", "[PRX]": "allenai/specter2_proximity"}
model = Model(variant="adapters", base_checkpoint="allenai/specter2", adapters_load_from=adapters_dict, all_tasks=["[CLF]", "[QRY]", "[RGN]", "[PRX]"])

Follow Step 2 onwards in the provided ReadMe.

Training

The training and validation triplets have been added to the SciRepEval benchmark, and is available here. The training data consists of triplets from SciNCL as a subset.

The training triplets cover the following fields of study:

Field of Study
Agricultural And Food Sciences
Art
Biology
Business
Chemistry
Computer Science
Economics
Education
Engineering
Environmental Science
Geography
Geology
History
Law
Linguistics
Materials Science
Mathematics
Medicine
Philosophy
Physics
Political Science
Psychology
Sociology

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
• 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

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	72.0	67.5	(30.6, 25.5)
SciNCL	55.6	73.4	68.8	(32.6, 27.3)
SciRepEval-Adapters	61.9	73.8	70.7	(35.3, 29.6)
SPECTER2 Base	56.3	73.6	69.1	(38.0, 32.4)
SPECTER2-Adapters	62.3	74.1	71.1	(38.4, 33.0)

The per task evaluation result can be found in this spreadsheet.

Citation

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