Token Classification
Transformers
Safetensors
English
bert
ner
named-entity-recognition
text-classification
sequence-labeling
transformer
nlp
pretrained-model
dataset-finetuning
deep-learning
huggingface
conll2025
real-time-inference
efficient-nlp
high-accuracy
gpu-optimized
chatbot
information-extraction
search-enhancement
knowledge-graph
legal-nlp
medical-nlp
financial-nlp
Instructions to use boltuix/NeuroBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boltuix/NeuroBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="boltuix/NeuroBERT-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroBERT-NER") model = AutoModelForTokenClassification.from_pretrained("boltuix/NeuroBERT-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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tags:
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- token-classification
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- ner
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base_model:
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license: apache-2.0
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datasets:
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- custom
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- chatgpt
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language:
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- en
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tags:
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- token-classification
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- ner
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- named-entity-recognition
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- text-classification
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- sequence-labeling
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- transformer
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- bert
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- nlp
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- pretrained-model
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- dataset-finetuning
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- deep-learning
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- huggingface
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- conll2012
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- real-time-inference
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- efficient-nlp
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- high-accuracy
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- gpu-optimized
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- chatbot
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- information-extraction
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- search-enhancement
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- knowledge-graph
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- legal-nlp
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- medical-nlp
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- financial-nlp
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base_model:
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- boltuix/bert-lite
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---
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