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--- |
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title: CAROLL NER Demos |
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emoji: 🐠 |
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colorFrom: purple |
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colorTo: pink |
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sdk: streamlit |
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sdk_version: 1.36.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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#### German Legal NER: |
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This language model is trained on the [Legal Entity Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition) dataset. We conducted a stratified 10-fold cross-validation to prevent overfitting. The results showed that their fine-tuned German BERT model outperformed the existing BiLSTM-CRF+ model, which was previously used on the same LER dataset. It is capable of annotating German legal data with the following 19 distinct labels: |
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|Abbreviation|Class| |
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|----|----| |
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|PER|Person| |
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|RR|Judge| |
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|AN|Lawyer| |
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|LD|Country| |
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|ST|City| |
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|STR|Street| |
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|LDS|Landscape| |
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|ORG|Organization| |
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|UN|Company| |
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|INN|Institution| |
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|GRT|Court| |
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|MRK|Brand| |
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|GS|Law| |
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|VO|Ordinance| |
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|EUN|European legal norm| |
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|VS|Regulation| |
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|VT|Contract| |
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|RS|Court decision| |
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|LIT|Legal literature| |
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This model is publicly available at [PaDaS-Lab/gbert-legal-ner](https://huggingface.co/PaDaS-Lab/gbert-legal-ner). We have also published a corresponding [paper](https://arxiv.org/pdf/2303.05388.pdf) in this regard. Please cite this paper while using this model: |
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```bibtex |
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@conference{icaart23, |
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author={Harshil Darji. and Jelena Mitrović. and Michael Granitzer.}, |
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title={German BERT Model for Legal Named Entity Recognition}, |
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booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,}, |
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year={2023}, |
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pages={723-728}, |
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publisher={SciTePress}, |
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organization={INSTICC}, |
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doi={10.5220/0011749400003393}, |
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isbn={978-989-758-623-1}, |
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issn={2184-433X}, |
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} |
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``` |
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--- |
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#### GDPR Privacy Policy NER: |
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This language model is trained on a privacy policy dataset. This dataset is annotated using 33 labels that are in accordance with GDPR. This model aims to facilitate information extraction related to GDPR from a given privacy policy. It can also be further improved to verify whether a given privacy policy follows the GDPR regulations. As stated above, this model is capable of annotating given privacy policy-related text with the following 33 labels: |
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|Abbreviation|Class| |
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|----|----| |
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|DC|Data Controller| |
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|DP|Data Processor| |
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|DPO|Data Protection Officer| |
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|R|Recipient| |
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|TP|Third Party| |
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|A|Authority| |
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|DS|Data Subject| |
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|DSO|Data Source| |
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|RP|Required Purpose| |
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|NRP|Not-Required Purpose| |
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|P|Processing| |
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|NPD|Non-Personal Data| |
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|PD|Personal Data| |
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|OM|Organisational Measure| |
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|TM|Technical Measure| |
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|LB|Legal Basis| |
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|CONS|Consent| |
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|CONT|Contract| |
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|LI|Legitimate Interest| |
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|ADM|Automated Decision Making| |
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|RET|Retention| |
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|SEU|Scale EU| |
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|SNEU|Scale Non-EU| |
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|RI|Right| |
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|DSR15|Art. 15 Right of access by the data subject| |
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|DSR16|Art. 16 Right to rectification| |
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|DSR17|Art. 17 Right to erasure ("right to be forgotten")| |
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|DSR18|Art. 18 Right to restriction of processing| |
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|DSR19|Art. 19 Notification obligation regarding rectification or erasure of personal data or restriction of processing| |
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|DSR20|Art. 20 Right to data portability| |
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|DSR21|Art. 21 Right to object| |
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|DSR22|Art. 22 Automated individual decision-making, including profiling| |
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|LC|Lodge Complaint| |
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This model is publicly available at [PaDaS-Lab/gdpr-privacy-policy-ner](https://huggingface.co/PaDaS-Lab/gdpr-privacy-policy-ner). |
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