Token Classification
Transformers
Safetensors
distilbert
citation
science
ner
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  example_title: Example 3
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  ---
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- # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
 
 
 
 
 
 
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- ## Model Details
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- ### Model Description
 
 
 
 
 
 
 
 
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
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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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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
 
 
 
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
 
 
 
 
 
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Software
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- ## Citation [optional]
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- **BibTeX:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  example_title: Example 3
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  ---
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+ # Citation Parsing (NER)
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  <!-- Provide a quick summary of what the model is/does. -->
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+ 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.
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+
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+ ## Overview
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+ <details>
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+ <summary>Click to expand</summary>
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+ - **Model type:** Language Model
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+ - **Architecture:** DistilBERT
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+ - **Language:** Multilingual
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+ - **License:** Apache 2.0
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+ - **Task:** Named Entity Recognition (NER) for Citation Parsing
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+ - **Dataset:** Custom Citation Parsing Dataset
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+ - **Additional Resources:**
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+ - [GitHub](https://github.com/sirisacademic/citation-parser)
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+ </details>
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+ ## Model description
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+ 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:
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+ - `TITLE`
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+ - `AUTHORS`
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+ - `VOLUME`
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+ - `ISSUE`
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+ - `YEAR`
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+ - `DOI`
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+ - `ISSN`
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+ - `ISBN`
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+ - `FIRST_PAGE`
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+ - `LAST_PAGE`
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+ - `JOURNAL`
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+ - `EDITOR`
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+ This model was trained using the **DistilBERT-base-multilingual-cased** architecture, making it capable of processing multilingual citation data.
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+ ## Intended Usage
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+ 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.
 
 
 
 
 
 
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+ ## How to use
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+ ```python
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+ from transformers import pipeline
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+ # Load the model
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+ citation_parser = pipeline("ner", model="SIRIS-Lab/citation-parser-ENTITY")
 
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+ # Example citation text
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+ citation_text = "MURAKAMI, H等: 'Unique thermal behavior of acrylic PSAs bearing long alkyl side groups and crosslinked by aluminum chelate', 《EUROPEAN POLYMER JOURNAL》"
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+ # Parse the citation
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+ result = citation_parser(citation_text)
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+ print(result)
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+ ```
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+ ## Training
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+ The model was trained using the `SIRIS-Lab/citation-parser-ENTITY` dataset consisting of:
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+ - **Training data**: 2419 samples
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+ - **Test data**: 269 samples
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+ The following hyperparameters were used for training:
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+ - **Base Model**: `distilbert/distilbert-base-multilingual-cased`
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+ - **Batch Size**: 16
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+ - **Number of Epochs**: 10
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+ - **Learning Rate**: 2e-5
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+ - **Weight Decay**: 0.01
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+ - **Max Sequence Length**: 512
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+ ## Evaluation Metrics
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+ The model's performance was evaluated on the test set, and the following results were obtained:
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+ | Metric | Value |
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+ |----------------------|---------|
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+ | **Overall Precision** | 0.9448 |
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+ | **Overall Recall** | 0.9548 |
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+ | **Overall F1** | 0.9498 |
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+ | **Overall Accuracy** | 0.9759 |
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+ ### Class-wise Evaluation Metrics:
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+ | Entity | Precision | Recall | F1 | Samples |
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+ |----------------------------|-----------|---------|---------|-----------------------|
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+ | **ALL (overall avg)** | 0.9448 | 0.9548 | 0.9498 | 269 |
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+ |----------------------------|-----------|---------|---------|-----------------------|
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+ | **AUTHORS** | 0.9577 | 0.9468 | 0.9522 | 263 |
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+ | **DOI** | 0.8333 | 0.9091 | 0.8696 | 22 |
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+ | **ISBN** | 1.0000 | 1.0000 | 1.0000 | 3 |
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+ | **ISSN** | 1.0000 | 1.0000 | 1.0000 | 34 |
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+ | **ISSUE** | 0.9385 | 0.9683 | 0.9531 | 63 |
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+ | **JOURNAL** | 0.8819 | 0.9228 | 0.9019 | 259 |
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+ | **LINK_ONLINE_AVAILABILITY**| 0.3333 | 0.5000 | 0.4000 | 2 |
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+ | **PAGE_FIRST** | 1.0000 | 1.0000 | 1.0000 | 130 |
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+ | **PAGE_LAST** | 0.9915 | 0.9832 | 0.9873 | 119 |
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+ | **PUBLICATION_YEAR** | 0.9797 | 0.9732 | 0.9764 | 149 |
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+ | **PUBLISHER** | 0.4231 | 0.5238 | 0.4681 | 21 |
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+ | **TITLE** | 0.9911 | 0.9867 | 0.9889 | 226 |
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+ | **VOLUME** | 0.9597 | 0.9520 | 0.9558 | 125
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+ ## Additional Information
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+ ### Authors
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+ SIRIS Lab, Research Division of SIRIS Academic.
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+ ### License
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+ This work is distributed under an [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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+ ### Contact
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+ For further information, send an email to either [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).