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- adapter-transformers
- bert
- allenai/scirepeval
<!-- Provide a quick summary of what the model is/does. -->
SPECTER2 is a family of models that succeeds [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
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](https://huggingface.co/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.
**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|
2. **We have a parallel version (termed [aug2023refresh](https://huggingface.co/allenai/specter2_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.**
# Adapter `allenai/specter2` for allenai/specter2_base
An [adapter](https://adapterhub.ml) for the [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
This adapter was created for usage with the **[adapters](https://github.com/adapter-hub/adapters)** library.
## Adapter Usage
First, install `adapters`:
pip install -U adapters
_Note: adapters is built as an add on to transformers and acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/specter2_base")
adapter_name = model.load_adapter("allenai/specter2", source="hf", set_active=True)
# Model Details
## Model Description
SPECTER2 has been trained on over 6M triplets of scientific paper citations, which are available [here](https://huggingface.co/datasets/allenai/scirepeval/viewer/cite_prediction_new/evaluation).
Post that it is trained with additionally attached task format specific adapter modules on all the [SciRepEval](https://huggingface.co/datasets/allenai/scirepeval) training tasks.
Task Formats trained on:
- Classification
- Regression
- Proximity (Retrieval)
- Adhoc Search
This is a retrieval specific adapter. For tasks where given a paper query, other relevant papers have to be retrieved from a corpus, use this adapter to generate the embeddings.
It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://api.semanticscholar.org/CorpusID:254018137) 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](https://huggingface.co/allenai/scibert_scivocab_uncased).
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/allenai/SPECTER2](https://github.com/allenai/SPECTER2)
- **Paper:** [https://api.semanticscholar.org/CorpusID:254018137](https://api.semanticscholar.org/CorpusID:254018137)
- **Demo:** [Usage](https://github.com/allenai/SPECTER2_0/blob/main/README.md)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
|Model|Name and HF link|Description|
|Proximity*|[allenai/specter2](https://huggingface.co/allenai/specter2)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
|Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with the proximity adapter)|
|Classification|[allenai/specter2_classification](https://huggingface.co/allenai/specter2_classification)|Encode papers to feed into linear classifiers as features|
|Regression|[allenai/specter2_regression](https://huggingface.co/allenai/specter2_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
# 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="specter2", 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_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, :]
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
For evaluation and downstream usage, please refer to [https://github.com/allenai/scirepeval/blob/main/evaluation/INFERENCE.md](https://github.com/allenai/scirepeval/blob/main/evaluation/INFERENCE.md).
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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](https://huggingface.co/datasets/allenai/scirepeval).
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](https://api.semanticscholar.org/CorpusID:215768677).
### Training Hyperparameters
The model is trained in two stages using [SciRepEval](https://github.com/allenai/scirepeval/blob/main/training/TRAINING.md):
- 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](https://github.com/allenai/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](https://github.com/zoranmedic/mdcr), a large scale citation recommendation benchmark.
|Model|SciRepEval In-Train|SciRepEval Out-of-Train|SciRepEval Avg|MDCR(MAP, Recall@5)|
|[BM-25](https://api.semanticscholar.org/CorpusID:252199740)|n/a|n/a|n/a|(33.7, 28.5)|
|[SPECTER](https://huggingface.co/allenai/specter)|54.7|57.4|68.0|(30.6, 25.5)|
|[SciNCL](https://huggingface.co/malteos/scincl)|55.6|57.8|69.0|(32.6, 27.3)|
|[SciRepEval-Adapters](https://huggingface.co/models?search=scirepeval)|61.9|59.0|70.9|(35.3, 29.6)|
|[SPECTER2 Base](allenai/specter2_base)|56.3|73.6|69.1|(38.0, 32.4)|
|[SPECTER2-Adapters](https://huggingface.co/models?search=allenai/specter-2)|**62.3**|**59.2**|**71.2**|**(38.4, 33.0)**|
Please cite the following works if you end up using SPECTER 2.0:
[SciRepEval paper](https://api.semanticscholar.org/CorpusID:254018137)
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},