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- tokenizer_config.json +5 -0
README.md
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---
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---
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language:
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- multilingual
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- pl
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- ru
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- uk
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- bg
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- cs
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- sl
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datasets:
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- SlavicNER
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license: apache-2.0
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library_name: transformers
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pipeline_tag: token-classification
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tags:
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- ner
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- named entity recognition
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---
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# Model description
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This is a baseline model for named entity **recognition** trained on the cross-topic split of the
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[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
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# Resources and Technical Documentation
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- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
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- Annotation guidelines: https://arxiv.org/pdf/2404.00482
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- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
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# Evaluation
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*Will appear soon*
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# Usage
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*Will appear soon*
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# Citation
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```latex
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@inproceedings{piskorski-etal-2024-cross-lingual,
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title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
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author = "Piskorski, Jakub and
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Marci{\'n}czuk, Micha{\l} and
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Yangarber, Roman",
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editor = "Calzolari, Nicoletta and
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Kan, Min-Yen and
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Hoste, Veronique and
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Lenci, Alessandro and
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Sakti, Sakriani and
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Xue, Nianwen",
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booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
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month = may,
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year = "2024",
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address = "Torino, Italy",
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publisher = "ELRA and ICCL",
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url = "https://aclanthology.org/2024.lrec-main.369",
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pages = "4143--4157",
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abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
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and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
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Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
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Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
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{---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
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with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
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and mT5-large for named entity lemmatization and linking.",
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}
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```
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<s>",
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"tokenizer_class": "XLMRobertaTokenizer",
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"unk_token": "<unk>"
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}
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{
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<<<<<<< HEAD
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"added_tokens_decoder": {
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"0": {
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"content": "<s>",
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"tokenizer_class": "XLMRobertaTokenizer",
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"unk_token": "<unk>"
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}
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=======
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"name_or_path": "FacebookAI/xlm-roberta-large"
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}
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>>>>>>> a71c7b1a8227f8d9719a94284200a65633e0d444
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