evamxb
/

soft-cite-intent-cls

@@ -1,199 +1,76 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 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.