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---
license: cc-by-4.0
datasets:
- wikiann
language:
- pl
pipeline_tag: token-classification
widget:
- text: "Jestem Krzysiek i pracuję w Ministerstwie Sportu"
- text: "Wiktoria pracuje w Krakowie, na AGH"
- text: "Nazywam się Grzegorz Jasiński, pochodzę ze Szczebrzeszyna"
---
# herbert-base-ner
## Model description
**herbert-base-ner** is a fine-tuned HerBERT model that can be used for **Named Entity Recognition** .
It has been trained to recognize three types of entities: person (PER), location (LOC) and organization (ORG).
Specifically, this model is an [*allegro/herbert-base-cased*](https://huggingface.co/allegro/herbert-base-cased) model that was fine-tuned on the Polish subset of *wikiann* dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2006
- Precision: 0.8886
- Recall: 0.9059
- F1: 0.8972
- Accuracy: 0.9569
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("pietruszkowiec/herbert-base-ner")
model = AutoModelForTokenClassification.from_pretrained("pietruszkowiec/herbert-base-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nazywam się Grzegorz Jasiński, pochodzę ze Szczebrzeszyna"
ner_results = nlp(example)
print(ner_results)
```
### BibTeX entry and citation info
```
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
```
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
```
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