Update README.md

README.md

@@ -1,105 +1,84 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- allenai/peer_read
+language:
+- en
+metrics:
+- accuracy
+- f1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for PaperPub
 
+*Paper pub*lication prediction based on English computer science abstracts.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+PaperPub is a SciBERT ([Beltagy et al 2019](https://arxiv.org/abs/1903.10676)) model fine-tuned to predict paper acceptance from computer science abstracts. 
+Acceptance is modeled as a binary decision of accept or reject.
+The training and evaluation data is based on the arXiv subsection of PeerRead ([Kang et al. 2018](https://aclanthology.org/N18-1149/)). 
+Our main use case for PaperPub is to research how attribution scores derived from acceptance predictions can inform reflecting about content and writing quality of abstracts. 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Semantic Computing Group, Bielefeld University, in particular Jan-Philipp Töberg, Christoph Düsing, Jonas Belouadi and Matthias Orlikowski
+- **Model type:** BERT for binary classification
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** SciBERT
 
-- **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]
+### Model Sources
 
-### Model Sources [optional]
+We will add a public demo of PaperPub used in an application which uses attribution scores to highlight words in an abstract that contribute to acceptance/rejection predcitions.
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** tba
+- **Demo:** tba
 
 ## 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. -->
+PaperPub can only be meaningfully used in a research setting. The model should not be used for any consequential paper quality judgements.
 
 ### 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]
+The intended use case in research into how attribution scores computed from paper acceptance decisions reflect the abstract's content quality.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model must not be used as part of any type of paper quality judgements, but in particular not in a peer review process. PaperPub is explicitly not meant to automate paper acceptance decisions.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Bias, Risks, and Limitations are mainly related to the used datset. In addition to limitations that apply to the SciBERT pre-training corpus, our training data represents
+only a very specific subset of papers.
 
 ### 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.
+Users should be aware that the dataset (computer science arXive preprints from a specific period) used for fine-tuning represents a very specific idea of what papers 
+and in particular papers fit for publication look like.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+tba
 
 ## 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]
+Custom stratified split of the arXiv subsection of PeerRead ([Kang et al. 2018](https://aclanthology.org/N18-1149/)). We use the data from their GitHub repository, not the Huggingface Hub version.
 
 ### 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]
-
+- **Training regime:** bf16 mixed precision
+- **Epochs:** 2
+- **Initial Learning Rate:** 2^-5
+ 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
@@ -108,47 +87,32 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+Custom stratified split of the arXiv subsection of PeerRead ([Kang et al. 2018](https://aclanthology.org/N18-1149/)). We use the data from their GitHub repository, not the Huggingface Hub version.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Models, we compare to a naive most-frequent-class baseline.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Accuracy, Macro F1
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
+|Model|Acc.|Macro F1|
+|---|---|---|---|---|
+|Majority Baseline|0.75|0.43|
+|SciBERT|0.82|0.76|
 
-## 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]
+- **Hardware Type:** 1xA40
+- **Hours used:** 0.3
+- **Cloud Provider:** Private Infrastructure
+- **Compute Region:** Europe
 
 ## Technical Specifications [optional]
 
@@ -158,42 +122,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+We are using an internal SLURM cluster with A40 GPUs
 
-<!-- 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]
-
-**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]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+tba
 
 ## Model Card Contact
 
-[More Information Needed]
+[Matthias Orlikowski](https://orlikow.ski)