Create README.md

README.md

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+---
+license: mit
+language:
+- bg
+- en
+- fr
+- de
+- ru
+- es
+- sw
+- tr
+- vi
+base_model:
+- rustemgareev/mdeberta-v3-base-lite
+pipeline_tag: token-classification
+tags:
+- deberta
+- deberta-v3
+- mdeberta
+- ner
+---
+
+# mdeberta-ontonotes5
+
+This is a multilingual DeBERTa model fine-tuned for Named Entity Recognition (NER) task.
+It is based on the [rustemgareev/mdeberta-v3-base-lite](https://huggingface.co/rustemgareev/mdeberta-v3-base-lite) model.
+
+## Usage
+
+```python
+from transformers import pipeline
+
+# Initialize the NER pipeline
+ner_pipeline = pipeline(
+    "token-classification",
+    model="rustemgareev/mdeberta-ontonotes5",
+    aggregation_strategy="simple"
+)
+
+# Example text
+text = "Apple Inc. is looking at buying a U.K. startup for $1 billion in London next week."
+
+# Get predictions
+entities = ner_pipeline(text)
+
+# Print the results
+for entity in entities:
+    print(f"Entity: {entity['word']}, Label: {entity['entity_group']}, Score: {entity['score']:.4f}")
+
+# Expected output:
+# Entity: Apple Inc., Label: ORGANIZATION, Score: 0.9975
+# Entity: U.K., Label: GPE, Score: 0.9956
+# Entity: $1 billion, Label: MONEY, Score: 0.9981
+# Entity: London, Label: GPE, Score: 0.9981
+# Entity: next week, Label: DATE, Score: 0.9940
+```
+
+## License
+
+This model is distributed under the [MIT License](https://opensource.org/licenses/MIT).