| | --- |
| | library_name: transformers |
| | license: mit |
| | datasets: |
| | - evamxb/soft-cite-intent |
| | language: |
| | - en |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # Software Citation Intent Classifier |
| |
|
| | The Software Citation Intent Classifier (`soft-cite-intent-cls`) can be used to predict the citation or "reference" intent |
| | behind a text reference or citation to a piece of software in an academic article. |
| |
|
| | Possible values include: |
| |
|
| | - `used` |
| | - `mentioned` |
| | - `created` |
| | - `other` |
| |
|
| | For example, given the sentence: "The XYZ code and software, with an example input dataset and detailed instructions are available from GitHub (https://github.com/user/repo)" |
| | should be predicted as "created" as the authors are directly referencing their own created code. |
| |
|
| | _The specific name of the code and software and username and repository have been removed for privacy._ |
| |
|
| | In comparison, the sentence: "For the statistical analyses of the data in this study, the Statistical Package for the Social Sciences (SPSS) version 22 (IBM Corp, Armonk, New York) was used" |
| | should be predicted as "used" as the authors are directly informing the reader that this is not their own software but rather software they used for analysis. |
| |
|
| | This was originally created during the [CZI Software Impact Hackathon](https://github.com/chanzuckerberg/software-impact-hackathon-2023) by Ana-Maria Istrate, Joshua Fisher, Xinyu Yang, Kara Moraw, Kai Li, Donghui Li, and Martin Klein. |
| |
|
| | Their original work can be found in the [SoftwareCitationIntent Repository](https://github.com/karacolada/SoftwareImpactHackathon2023_SoftwareCitationIntent). |
| |
|
| | Eva Maxfield Brown recreated and uploaded this version of the model to Huggingface Hub for her own work, her scripts for recreating this model can be found in the [grobid-soft-proc repository](https://github.com/evamaxfield/grobid-soft-proc). |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| |
|
| | <!-- Provide a longer summary of what this model is. --> |
| |
|
| | - **Developed by:** Originally created by Ana-Maria Istrate, Joshua Fisher, Xinyu Yang, Kara Moraw, Kai Li, Donghui Li, and Martin Klein. |
| | - **Made Available by** Eva Maxfield Brown |
| | - **Language(s) (NLP):** en (English) |
| | - **License:** MIT |
| | - **Finetuned from model [optional]:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) |
| |
|
| | ### Model Sources [optional] |
| |
|
| | <!-- Provide the basic links for the model. --> |
| |
|
| | - **Repository:** [Original Repository](https://github.com/karacolada/SoftwareImpactHackathon2023_SoftwareCitationIntent), [Distribution Repository](https://github.com/evamaxfield/grobid-soft-proc/tree/main/training) |
| | - **Paper [optional]:** [Scientific Software Citation Intent Classification using Large Language Models](https://github.com/NFDI4DS/nslp2024/blob/main/accepted_papers/NSLP_2024_paper_20.pdf) |
| |
|
| | ## Training Details |
| |
|
| | ### Training Data |
| |
|
| | - **HuggingFace Dataset:** [soft-cite-intent](https://huggingface.co/datasets/evamxb/soft-cite-intent) |
| | - **CSV from Repo:** [soft-cite-intent](https://github.com/evamaxfield/grobid-soft-proc/blob/main/training/sci-impact-hack-soft-cite-intent.csv) |
| |
|
| | ### Training Procedure |
| |
|
| | - **Training Script:** [train-and-upload-best](https://github.com/evamaxfield/grobid-soft-proc/blob/main/training/train-and-upload-best.py) |
| |
|
| | ### Results |
| |
|
| | - Accuracy: 0.916 |
| | - Precision: 0.916 |
| | - Recall: 0.916 |
| | - F1: 0.916 |
| |
|
| | ## Citation [optional] |
| |
|
| | See their paper [Scientific Software Citation Intent Classification using Large Language Models](https://github.com/NFDI4DS/nslp2024/blob/main/accepted_papers/NSLP_2024_paper_20.pdf) at NSLP2024. |
