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--- |
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language: tr |
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datasets: |
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- SUNLP-NER-Twitter |
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# berturk-sunlp-ner-turkish |
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## Introduction |
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[berturk-sunlp-ner-turkish] is a NER model that was fine-tuned from the BERTurk-cased model on the SUNLP-NER-Twitter dataset. |
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## Training data |
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The model was trained on the SUNLP-NER-Twitter dataset (5000 tweets). The dataset can be found at https://github.com/SU-NLP/SUNLP-Twitter-NER-Dataset |
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Named entity types are as follows: |
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Person, Location, Organization, Time, Money, Product, TV-Show |
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## How to use berturk-sunlp-ner-turkish with HuggingFace |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("busecarik/berturk-sunlp-ner-turkish") |
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model = AutoModelForTokenClassification.from_pretrained("busecarik/berturk-sunlp-ner-turkish") |
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``` |
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## Model performances on SUNLP-NER-Twitter test set (metric: seqeval) |
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Precision|Recall|F1 |
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-|-|- |
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85.08|84.46|84.77 |
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Classification Report |
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Entity|Precision|Recall|F1 |
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LOCATION|0.75|0.80|0.78 |
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MONEY|0.74|0.59|0.65 |
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ORGANIZATION|0.82|0.86|0.84 |
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PERSON|0.94|0.91|0.92 |
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PRODUCT|0.52|0.44|0.48 |
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TIME|0.88|0.87|0.87 |
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TVSHOW|0.65|0.58|0.61 |
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