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onnx-community
/
NeuroBERT-Pro-ONNX

Text Classification
Transformers.js
ONNX
Transformers
English
bert
fill-mask
BERT
NeuroBERT
transformer
nlp
neurobert-pro
edge-ai
low-resource
micro-nlp
quantized
iot
wearable-ai
offline-assistant
intent-detection
real-time
smart-home
embedded-systems
command-classification
toy-robotics
voice-ai
eco-ai
english
flagship
mobile-nlp
ner
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use onnx-community/NeuroBERT-Pro-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers.js

    How to use onnx-community/NeuroBERT-Pro-ONNX with Transformers.js:

    // npm i @huggingface/transformers
    import { pipeline } from '@huggingface/transformers';
    
    // Allocate pipeline
    const pipe = await pipeline('text-classification', 'onnx-community/NeuroBERT-Pro-ONNX');
  • Transformers

    How to use onnx-community/NeuroBERT-Pro-ONNX with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="onnx-community/NeuroBERT-Pro-ONNX")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("onnx-community/NeuroBERT-Pro-ONNX")
    model = AutoModelForMaskedLM.from_pretrained("onnx-community/NeuroBERT-Pro-ONNX")
  • Notebooks
  • Google Colab
  • Kaggle
NeuroBERT-Pro-ONNX
582 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
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Felladrin
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  • .gitattributes
    1.52 kB
    initial commit 2 months ago
  • README.md
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  • config.json
    671 Bytes
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  • quantize_config.json
    310 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    711 kB
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  • tokenizer_config.json
    1.3 kB
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  • vocab.txt
    232 kB
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