| | --- |
| | 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) |
| | ``` |
| |
|
| | ## Training |
| |
|
| | The model was trained using the **Citation Pre-Screening Dataset** consisting of: |
| |
|
| | - **Training data**: 3599 samples |
| | - **Test data**: 400 samples |
| |
|
| | The following hyperparameters were used for training: |
| |
|
| | - **Model Path**: `distilbert/distilbert-base-multilingual-cased` |
| | - **Batch Size**: 32 |
| | - **Number of Epochs**: 4 |
| | - **Learning Rate**: 2e-5 |
| | - **Max Sequence Length**: 512 |
| |
|
| | ## Evaluation Metrics |
| |
|
| | The model's performance was evaluated on the test set, and the following results were obtained: |
| |
|
| | | Metric | Value | |
| | |----------------------|--------| |
| | | **Accuracy** | 0.95 | |
| | | **Macro avg F1** | 0.94 | |
| | | **Weighted avg F1** | 0.95 | |
| |
|
| | ## Additional information |
| |
|
| | ### Authors |
| |
|
| | - SIRIS Lab, Research Division of SIRIS Academic. |
| |
|
| | ### License |
| |
|
| | This work is distributed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
| |
|
| | ### Contact |
| | For further information, send an email to either [nicolau.duransilva@sirisacademic.com](mailto:nicolau.duransilva@sirisacademic.com) or [info@sirisacademic.com](mailto:info@sirisacademic.com). |
| |
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| |
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