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 vllm-project/NeuroBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-project/NeuroBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vllm-project/NeuroBERT-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vllm-project/NeuroBERT-NER") model = AutoModelForTokenClassification.from_pretrained("vllm-project/NeuroBERT-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vllm-project/NeuroBERT-NER: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/vllm-project/NeuroBERT-NER/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-project/NeuroBERT-NER/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/vllm-project/NeuroBERT-NER/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.