Instructions to use JonyC/scibert-NER-finetuned-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonyC/scibert-NER-finetuned-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="JonyC/scibert-NER-finetuned-improved")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("JonyC/scibert-NER-finetuned-improved") model = AutoModelForTokenClassification.from_pretrained("JonyC/scibert-NER-finetuned-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
updated the id2label
Browse files
README.md
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@@ -25,6 +25,7 @@ import spacy
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nlp = spacy.load("en_core_web_sm")
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tokenizer = AutoTokenizer.from_pretrained("JonyC/scibert-NER-finetuned-improved")
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model = AutoModelForTokenClassification.from_pretrained("JonyC/scibert-NER-finetuned-improved")
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def predict_scibert_labels(sentence):
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# Step 1: SpaCy tokenization
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nlp = spacy.load("en_core_web_sm")
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tokenizer = AutoTokenizer.from_pretrained("JonyC/scibert-NER-finetuned-improved")
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model = AutoModelForTokenClassification.from_pretrained("JonyC/scibert-NER-finetuned-improved")
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id2label = model.config.id2label
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def predict_scibert_labels(sentence):
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# Step 1: SpaCy tokenization
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