Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
named-entity-recognition
sequence-tagger-model
Instructions to use Babelscape/wikineural-multilingual-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babelscape/wikineural-multilingual-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Babelscape/wikineural-multilingual-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner") model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner")
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model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner")
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nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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example = "My name is Wolfgang and I live in Berlin"
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ner_results = nlp(example)
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tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner")
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model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner")
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nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
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example = "My name is Wolfgang and I live in Berlin"
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ner_results = nlp(example)
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