Adding model card for specter2-review-method

README.md

@@ -1,199 +1,120 @@
 ---
 library_name: transformers
-tags: []
+metrics:
+- f1
+base_model:
+- allenai/specter2_base
+model-index:
+  - name: specter2-review-method
+    results:
+      - task:
+          type: text-classification
+        dataset:
+          name: validation
+          type: validation
+        metrics:
+          - name: macro-average F1-score
+            type: macro-average F1-score
+            value: 0.83
 ---
 
-# Model Card for Model ID
+# Model: specter2-review-method
 
-<!-- Provide a quick summary of what the model is/does. -->
+The model `snsf-data/specter2-review-method` 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 methods to be used in 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:
 
-## Model Details
+- Loss function: cross-entropy loss
+- Optimizer: AdamW
+- Weight decay: 0.01
+- Learning rate: 2e-5
+- Epochs: 3
+- Batch size: 10
+- GPU: NVIDIA RTX A2000
 
-### Model Description
+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: 0.83** was achieved on the validation set. The share of the outcome label amounts to 16.2%.
 
-<!-- Provide a longer summary of what this model is. -->
+The fine-tuning codes are open-sourced on GitHub: https://github.com/snsf-data/ml-peer-review-analysis .
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+For more details, see the the following preprint:
 
-### Model Sources [optional]
+**A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports**
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+by [Gabriel Okasa](https://orcid.org/0000-0002-3573-7227),
+[Alberto de León](https://orcid.org/0009-0002-0401-2618),
+[Michaela Strinzel](https://orcid.org/0000-0003-3181-0623),
+[Anne Jorstad](https://orcid.org/0000-0002-6438-1979),
+[Katrin Milzow](https://orcid.org/0009-0002-8959-2534),
+[Matthias Egger](https://orcid.org/0000-0001-7462-5132), and
+[Stefan Müller](https://orcid.org/0000-0002-6315-4125), available on arXiv: https://arxiv.org/abs/2411.16662 .
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The model can be used to classify sentences from grant peer review reports for addressing the methods used in the proposed project.
 
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
+Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+# import transformers library
+import transformers
 
-#### Hardware
+# load tokenizer from specter2_base - the base model
+tokenizer = transformers.AutoTokenizer.from_pretrained("allenai/specter2_base")
 
-[More Information Needed]
+# load the SNSF fine-tuned model for classification of methods comments in review texts
+model = transformers.AutoModelForSequenceClassification.from_pretrained("snsf-data/specter2-review-method")
 
-#### Software
+# setup the classification pipeline
+classification_pipeline = transformers.TextClassificationPipeline(
+    model=model,
+    tokenizer=tokenizer,
+    return_all_scores=True
+)
 
-[More Information Needed]
+# prediction for an example review sentence addressing methods
+classification_pipeline("The reduced-form methods suggested in the proposal seem to be the appropriate method for the proposed projects.")
 
-## Citation [optional]
+# prediction for an example review sentence not addressing methods
+classification_pipeline("There are currently several activities on an international level that have identified the issue and activities are underway.")
+```
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```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:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+Okasa, G., de León, A., Strinzel, M., Jorstad, A., Milzow, K., Egger, M., & Müller, S. (2024). A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports. arXiv preprint arXiv:2411.16662.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+[Gabriel Okasa](https://orcid.org/0000-0002-3573-7227),
+[Alberto de León](https://orcid.org/0009-0002-0401-2618),
+[Michaela Strinzel](https://orcid.org/0000-0003-3181-0623),
+[Anne Jorstad](https://orcid.org/0000-0002-6438-1979),
+[Katrin Milzow](https://orcid.org/0009-0002-8959-2534),
+[Matthias Egger](https://orcid.org/0000-0001-7462-5132), and
+[Stefan Müller](https://orcid.org/0000-0002-6315-4125)
 
 ## Model Card Contact
 
-[More Information Needed]
+gabriel.okasa@snf.ch