---
library_name: transformers
license: apache-2.0
datasets:
- SIRIS-Lab/citation-parser-ENTITY
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
tags:
- citation
- science
- ner
base_model:
- distilbert/distilbert-base-multilingual-cased
---

# Citation Parsing (NER)

<!-- Provide a quick summary of what the model is/does. -->
The **Citation Parsing (NER)** model utilizes advanced Named Entity Recognition (NER) to extract key fields from citation texts. This model parses citations into structured data fields such as TITLE, AUTHORS, VOLUME, ISSUE, YEAR, DOI, ISSN, ISBN, FIRST_PAGE, LAST_PAGE, JOURNAL, and EDITOR.

## Overview

<details>
<summary>Click to expand</summary>

- **Model type:** Language Model
- **Architecture:** DistilBERT
- **Language:** Multilingual
- **License:** Apache 2.0
- **Task:** Named Entity Recognition (NER) for Citation Parsing
- **Dataset:** Custom Citation Parsing Dataset
- **Additional Resources:**
  - [GitHub](https://github.com/sirisacademic/citation-parser)
</details>

## Model description

The **Citation Parsing (NER)** model is part of the [`Citation Parser`](https://github.com/sirisacademic/citation-parser) package. It is fine-tuned for extracting structured information from citation texts into the following key fields:

- `TITLE`
- `AUTHORS`
- `VOLUME`
- `ISSUE`
- `YEAR`
- `DOI`
- `ISSN`
- `ISBN`
- `FIRST_PAGE`
- `LAST_PAGE`
- `JOURNAL`
- `EDITOR`

This model was trained using the **DistilBERT-base-multilingual-cased** architecture, making it capable of processing multilingual citation data.

## Intended Usage

This model is designed for extracting citation information and parsing raw citation text into structured fields. It is ideal for automating citation metadata extraction in academic databases, manuscript workflows, or citation analysis tools.

## How to use

```python
from transformers import pipeline

# Load the model
citation_parser = pipeline("ner", model="SIRIS-Lab/citation-parser-ENTITY")

# 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》"

# Parse the citation
result = citation_parser(citation_text)
print(result)
```

## Training

The model was trained using the `SIRIS-Lab/citation-parser-ENTITY` dataset consisting of:
- **Training data**: 2419 samples
- **Test data**: 269 samples

The following hyperparameters were used for training:

- **Base Model**: `distilbert/distilbert-base-multilingual-cased`
- **Batch Size**: 16
- **Number of Epochs**: 10
- **Learning Rate**: 2e-5
- **Weight Decay**: 0.01
- **Max Sequence Length**: 512

## Evaluation Metrics

The model's performance was evaluated on the test set, and the following results were obtained:

| Metric               | Value   |
|----------------------|---------|
| **Overall Precision** | 0.9448  |
| **Overall Recall**    | 0.9548  |
| **Overall F1**        | 0.9498  |
| **Overall Accuracy**  | 0.9759  |

### Class-wise Evaluation Metrics:

| Entity                     | Precision | Recall  | F1      | Samples |
|----------------------------|-----------|---------|---------|-----------------------|
| **ALL (overall avg)**                 |  0.9448    | 0.9548  | 0.9498  | 269                   |
|----------------------------|-----------|---------|---------|-----------------------|
| **AUTHORS**                 | 0.9577    | 0.9468  | 0.9522  | 263                   |
| **DOI**                     | 0.8333    | 0.9091  | 0.8696  | 22                    |
| **ISBN**                    | 1.0000    | 1.0000  | 1.0000  | 3                     |
| **ISSN**                    | 1.0000    | 1.0000  | 1.0000  | 34                    |
| **ISSUE**                   | 0.9385    | 0.9683  | 0.9531  | 63                    |
| **JOURNAL**                 | 0.8819    | 0.9228  | 0.9019  | 259                   |
| **LINK_ONLINE_AVAILABILITY**| 0.3333    | 0.5000  | 0.4000  | 2                     |
| **PAGE_FIRST**              | 1.0000    | 1.0000  | 1.0000  | 130                   |
| **PAGE_LAST**               | 0.9915    | 0.9832  | 0.9873  | 119                   |
| **PUBLICATION_YEAR**        | 0.9797    | 0.9732  | 0.9764  | 149                   |
| **PUBLISHER**               | 0.4231    | 0.5238  | 0.4681  | 21                    |
| **TITLE**                   | 0.9911    | 0.9867  | 0.9889  | 226                   |
| **VOLUME**                  | 0.9597    | 0.9520  | 0.9558  | 125   

## Additional Information

### Authors

SIRIS Lab, Research Division of SIRIS Academic.

### License

This work is distributed under an [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).