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
Download onnx/model_quantized.onnx from gnumanth/nullspan: direct link, hf CLI and curl.
- Browser
- Download file 347 MB
-
https://huggingface.co/gnumanth/nullspan/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://gnumanth/nullspan/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/gnumanth/nullspan/resolve/main/onnx/model_quantized.onnx
347 MB
- Xet hash:
- 24baabc6d72726630c3d218527bb63a8d6cb9ba6c627fdc322f12cadeaf330ce
- Size of remote file:
- 347 MB
- SHA256:
- c665775071ea89615ca69201d80b3437ef93391a039f12846722fa1b99765bf1
路
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