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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## 📜 Citation
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```bibtex
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@inproceedings{pradhan-etal-2012-conll,
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title = "CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes",
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author = "Pradhan, Sameer and Ramshaw, Lance and Weischedel, Ralph and MacCartney, Bill and Xue, Nianwen and Palmer, Martha",
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booktitle = "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
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year = "2012",
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url = "https://aclanthology.org/W12-4501"
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}
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```
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---
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## ✍️ Contact
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- 🧠 Author: Boltuix
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## ✍️ Contact
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- 🧠 Author: Boltuix
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