Question Answering
Transformers
ONNX
Safetensors
nullspan
feature-extraction
system-one
structured-extraction
span-extraction
calibrated-estimation
time-series-forecasting
distilbert
custom_code
Instructions to use gnumanth/nullspan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gnumanth/nullspan with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="gnumanth/nullspan", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gnumanth/nullspan", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
6 days ago
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docs: add 17-system JevBench SCIENCE_REPORT.md
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7 days ago
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Update Int8 quantized ONNX model (64.3 MB) for in-browser WASM SIMD runtime
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Upload fine-tuned nullspan 10-op System One extraction model (Apple Silicon MPS)
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Upload WordPiece tokenizer.json for browser ONNX inference
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Upload dynamically quantized Int8 ONNX model (64.3 MB) for browser WASM/WebGPU inference
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Upload fine-tuned nullspan 10-op System One extraction model (Apple Silicon MPS)
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Upload fine-tuned nullspan 10-op System One extraction model (Apple Silicon MPS)
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Upload fine-tuned nullspan System One extraction model (Apple Silicon MPS)
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Upload fine-tuned nullspan System One extraction model (Apple Silicon MPS)
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initial commit
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