Question Answering
Transformers
ONNX
Safetensors
nullspan
feature-extraction
system-one
typed-decisions
jevbench
structured-extraction
span-extraction
calibrated-estimation
electra
modernbert
custom_code
Eval Results
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
Yesterday
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Release nullspan v1.2.1: NullspanSystemOneEngine v2.1 (Typed Decisions 75.4% #2 Overall, JevBench Easy 100% 48/48) + SQuAD2-aligned NullspanForExtraction
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Release v1.2.0: 344.2M ELECTRA-Large + SchemaAdapterBlock trained on GCP V100 + 330.1 MB Int8 ONNX (#1 NullspanBench)
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5 days ago
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Release v1.1.0: 512-token sliding window + multi-numeric contract disambiguation
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feat: long-context windowing and multi-numeric contract disambiguation
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12 days ago
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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 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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