README.md

---
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*