library_name: transformers
metrics:
- f1
base_model:
- allenai/specter2_base
model-index:
- name: specter2-review-suitability
results:
- task:
type: text-classification
dataset:
name: validation
type: validation
metrics:
- name: macro-average F1-score
type: macro-average F1-score
value: 0.79
Model: specter2-review-suitability
The model snsf-data/specter2-review-suitability is based on the allenai/specter2_base model and fine-tuned for a binary classification task.
In particular, the model is fine-tuned to classify if a sentence from SNSF grant peer review report is addressing the following aspect:
Does the sentence address the suitability of the methods to be used within the proposed research project?
The model was fine-tuned based on a training set of 2'500 sentences from the SNSF grant peer review reports, which were manually annotated by multiple human annotators via majority rule. The fine-tuning was performed locally without access to the internet to prevent any potential data leakage or network interference. The following setup was used for the fine-tuning:
- Loss function: cross-entropy loss
- Optimizer: AdamW
- Weight decay: 0.01
- Learning rate: 2e-5
- Epochs: 3
- Batch size: 10
- GPU: NVIDIA RTX A2000
The model was then evaluated based on a validation set of 500 sentences, which were also manually annotated by multiple human annotators via majority rule. The resulting macro-average F1 score: 0.79 was achieved on the validation set. The share of the outcome label amounts to 8.5%.
The fine-tuning codes are open-sourced on GitHub: https://github.com/snsf-data/ml-peer-review-analysis .
Due to data privacy laws no data used for the fine-tuning can be publicly shared. For a detailed description of data protection please refer to the data management plan underlying this work: https://doi.org/10.46446/DMP-peer-review-assessment-ML. The annotation codebook is available online: https://doi.org/10.46446/Codebook-peer-review-assessment-ML.
For more details, see the the following preprint:
A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports
by Gabriel Okasa, Alberto de Le贸n, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger, and Stefan M眉ller, available on arXiv: https://arxiv.org/abs/2411.16662 .
How to Get Started with the Model
The model can be used to classify sentences from grant peer review reports for addressing the suitability of the methods in the proposed research project.
Use the code below to get started with the model.
# import transformers library
import transformers
# load tokenizer from specter2_base - the base model
tokenizer = transformers.AutoTokenizer.from_pretrained("allenai/specter2_base")
# load the SNSF fine-tuned model for classification of suitability of methods in review texts
model = transformers.AutoModelForSequenceClassification.from_pretrained("snsf-data/specter2-review-suitability")
# setup the classification pipeline
classification_pipeline = transformers.TextClassificationPipeline(
model=model,
tokenizer=tokenizer,
return_all_scores=True
)
# prediction for an example review sentence addressing suitability of methods
classification_pipeline("The meths are suitable and can be clearly achieved in this laboratory.")
# prediction for an example review sentence not addressing suitability of methods
classification_pipeline("There are currently several activities on an international level that have identified the issue and activities are underway.")
Citation
BibTeX:
@article{okasa2024supervised,
title={A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports},
author={Okasa, Gabriel and de Le{\'o}n, Alberto and Strinzel, Michaela and Jorstad, Anne and Milzow, Katrin and Egger, Matthias and M{\"u}ller, Stefan},
journal={arXiv preprint arXiv:2411.16662},
year={2024}
}
APA:
Okasa, G., de Le贸n, A., Strinzel, M., Jorstad, A., Milzow, K., Egger, M., & M眉ller, S. (2024). A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports. arXiv preprint arXiv:2411.16662.
Model Card Authors
Gabriel Okasa, Alberto de Le贸n, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger, and Stefan M眉ller