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---
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license: mit
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language:
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- bg
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- en
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- fr
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- de
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- ru
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- es
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- sw
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- tr
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- vi
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base_model:
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- rustemgareev/mdeberta-v3-base-lite
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pipeline_tag: token-classification
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tags:
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- deberta
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- deberta-v3
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- mdeberta
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- ner
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---
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# mdeberta-ner-ontonotes5
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This is a multilingual DeBERTa model fine-tuned for Named Entity Recognition (NER) task.
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It is based on the [rustemgareev/mdeberta-v3-base-lite](https://huggingface.co/rustemgareev/mdeberta-v3-base-lite) model.
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## Usage
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```python
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from transformers import pipeline
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# Initialize the NER pipeline
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ner_pipeline = pipeline(
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"token-classification",
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model="rustemgareev/mdeberta-ner-ontonotes5",
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aggregation_strategy="simple"
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)
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# Example text
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text = "Apple Inc. is looking at buying a U.K. startup for $1 billion in London next week."
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# Get predictions
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entities = ner_pipeline(text)
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# Print the results
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for entity in entities:
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print(f"Entity: {entity['word']}, Label: {entity['entity_group']}, Score: {entity['score']:.4f}")
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# Expected output:
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# Entity: Apple Inc., Label: ORGANIZATION, Score: 0.9989
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# Entity: U.K., Label: GPE, Score: 0.9983
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# Entity: $1 billion, Label: MONEY, Score: 0.9984
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# Entity: London, Label: GPE, Score: 0.9987
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# Entity: next week, Label: DATE, Score: 0.9957
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```
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## License
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This model is distributed under the [MIT License](https://opensource.org/licenses/MIT). |
