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 special_tokens_map.json from gnumanth/nullspan: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/gnumanth/nullspan/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://gnumanth/nullspan/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/gnumanth/nullspan/resolve/main/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |