Adding model card for specter2-review-proposal

README.md

@@ -1,199 +1,120 @@
 ---
 library_name: transformers
-tags: []
+metrics:
+- f1
+base_model:
+- allenai/specter2_base
+model-index:
+  - name: specter2-review-proposal
+    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-proposal
 
-<!-- Provide a quick summary of what the model is/does. -->
+The model `snsf-data/specter2-review-proposal` 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 proposal or specific parts of it, as opposed to the applicant(s) or context beyond the proposal (such as the research field or the funding scheme’s objectives etc.)?***
 
+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 40.9%.
 
-<!-- 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 proposal itself, as opposed to the applicant.
 
-## 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 proposal comments in review texts
+model = transformers.AutoModelForSequenceClassification.from_pretrained("snsf-data/specter2-review-proposal")
 
-#### 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 proposal
+classification_pipeline("The proposal not only has a strong rationale, it also incorporates original ideas and questions that will be tested using modern approaches.")
 
-## Citation [optional]
+# prediction for an example review sentence not addressing proposal
+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]
+[email protected]