Token Classification
Transformers
PyTorch
Safetensors
English
French
German
stacked_bert
v1.0.0
custom_code
Instructions to use impresso-project/ner-stacked-bert-multilingual-v1.1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use impresso-project/ner-stacked-bert-multilingual-v1.1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="impresso-project/ner-stacked-bert-multilingual-v1.1.0", trust_remote_code=True)# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("impresso-project/ner-stacked-bert-multilingual-v1.1.0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
8e1e56c
1
Parent(s): bf56a9a
one filter
Browse files- generic_ner.py +1 -1
generic_ner.py
CHANGED
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@@ -784,7 +784,7 @@ class ExtendedMultitaskTimeModelForTokenClassificationPipeline(Pipeline):
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if DEBUG:
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print(all_entities)
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# print("After remove_included_entities:")
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-
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# if DEBUG:
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# print("After remove_included_entities:", all_entities)
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# all_entities = remove_trailing_stopwords(all_entities)
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if DEBUG:
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print(all_entities)
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# print("After remove_included_entities:")
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+
all_entities = remove_included_entities(all_entities)
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# if DEBUG:
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# print("After remove_included_entities:", all_entities)
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# all_entities = remove_trailing_stopwords(all_entities)
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