SIRIS-Lab
/

citation-parser-TYPE

@@ -1,199 +1,158 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+tags:
+- citation
+- text-classification
+- science
+license: apache-2.0
+language:
+  - af
+  - am
+  - ar
+  - as
+  - az
+  - be
+  - bg
+  - bn
+  - br
+  - bs
+  - ca
+  - cs
+  - cy
+  - da
+  - de
+  - el
+  - en
+  - eo
+  - es
+  - et
+  - eu
+  - fa
+  - fi
+  - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - he
+  - hi
+  - hr
+  - hu
+  - hy
+  - id
+  - is
+  - it
+  - ja
+  - jv
+  - ka
+  - kk
+  - km
+  - kn
+  - ko
+  - ku
+  - ky
+  - la
+  - lo
+  - lt
+  - lv
+  - mg
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - my
+  - ne
+  - nl
+  - 'no'
+  - om
+  - or
+  - pa
+  - pl
+  - ps
+  - pt
+  - ro
+  - ru
+  - sa
+  - sd
+  - si
+  - sk
+  - sl
+  - so
+  - sq
+  - sr
+  - su
+  - sv
+  - sw
+  - ta
+  - te
+  - th
+  - tl
+  - tr
+  - ug
+  - uk
+  - ur
+  - uz
+  - vi
+  - xh
+  - yi
+  - zh
+base_model:
+- distilbert/distilbert-base-multilingual-cased
 ---
+# Citation Pre-Screening
 <!-- Provide a quick summary of what the model is/does. -->
+## Overview
+<details>
+<summary>Click to expand</summary>
+- **Model type:** Language Model
+- **Architecture:** DistilBERT
+- **Language:** Multilingual
+- **License:** Apache 2.0
+- **Task:** Binary Classification (Citation Pre-Screening)
+- **Dataset:** SIRIS-Lab/citation-parser-TYPE
+- **Additional Resources:**
+  - [GitHub](https://github.com/sirisacademic/citation-parser)
+</details>
+## Model description
+The **Citation Pre-Screening** model is part of the [`Citation Parser`](https://github.com/sirisacademic/citation-parser) package and is fine-tuned for classifying citation texts as valid or invalid. This model, based on **DistilBERT**, is specifically designed for automated citation processing workflows, making it an essential component of the **Citation Parser** tool for citation metadata extraction and validation.
+The model was trained on a dataset containing citation texts, with the labels `True` (valid citation) and `False` (invalid citation). The dataset contains 3599 training samples and 400 test samples, with each example consisting of citation-related text and a corresponding label.
+The fine-tuning process was done with the **DistilBERT-base-multilingual-cased** architecture, making the model capable of handling multilingual text, but it was evaluated on English citation data.
+## Intended Usage
+This model is intended to classify raw citation text as either a valid or invalid citation based on the provided input. It is ideal for automating the pre-screening process in citation databases or manuscript workflows.
+## How to use
+```python
+from transformers import pipeline
+# Load the model
+citation_classifier = pipeline("text-classification", model="sirisacademic/citation-pre-screening")
+# Example citation text
+citation_text = "MURAKAMI, H等: 'Unique thermal behavior of acrylic PSAs bearing long alkyl side groups and crosslinked by aluminum chelate', 《EUROPEAN POLYMER JOURNAL》"
+# Classify the citation
+result = citation_classifier(citation_text)
+print(result)
+```
+## Additional information
+### Authors
+- SIRIS Lab, Research Division of SIRIS Academic.