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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-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-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: 0.9975 |
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# Entity: U.K., Label: GPE, Score: 0.9956 |
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# Entity: $1 billion, Label: MONEY, Score: 0.9981 |
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# Entity: London, Label: GPE, Score: 0.9981 |
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# Entity: next week, Label: DATE, Score: 0.9940 |
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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). |
