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---
language:
- multilingual
- pl
- ru
- uk
- bg
- cs
- sl
datasets:
- SlavicNER
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
tags:
- entity linking
widget:
- text: "pl:Polsce"
- text: "cs:Velké Británii"
- text: "bg:българите"
- text: "ru:Великобританию"
- text: "sl:evropske komisije"
- text: "uk:Європейського агентства лікарських засобів"
---
# Model description
This is a baseline model for named entity **lemmatization** trained on the single-out topic split of the
[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
# Resources and Technical Documentation
- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
- Annotation guidelines: https://arxiv.org/pdf/2404.00482
- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
# Evaluation
| **Language** | **Seq2seq** | **Support** |
|:------------:|:-----------:|-----------------:|
| PL | 75.13 | 2 549 |
| CS | 77.92 | 1 137 |
| RU | 67.56 | 18 018 |
| BG | 63.60 | 6 085 |
| SL | 76.81 | 7 082 |
| UK | 58.94 | 3 085 |
| All | 68.75 | 37 956 |
# Usage
You can use this model directly with a pipeline for text2text generation:
```python
from transformers import pipeline
model_name = "SlavicNLP/slavicner-linking-single-out-large"
pipe = pipeline("text2text-generation", model_name)
texts = ["pl:Polsce", "cs:Velké Británii", "bg:българите", "ru:Великобританию",
"sl:evropske komisije", "uk:Європейського агентства лікарських засобів"]
outputs = pipe(texts)
ids = [o['generated_text'] for o in outputs]
print(ids)
# ['GPE-Poland', 'GPE-Great-Britain', 'GPE-Bulgaria', 'GPE-Great-Britain',
# 'ORG-European-Commission', 'ORG-EMA-European-Medicines-Agency']
```
# Citation
*Will appear soon*
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