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
File size: 1,396 Bytes
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"added_tokens_decoder": {
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"single_word": false,
"special": true
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"2": {
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"single_word": false,
"special": true
},
"3": {
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"lstrip": false,
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},
"128000": {
"content": "[MASK]",
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"single_word": false,
"special": true
}
},
"bos_token": "[CLS]",
"clean_up_tokenization_spaces": false,
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"extra_special_tokens": {},
"mask_token": "[MASK]",
"max_length": 512,
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"split_by_punct": false,
"stride": 0,
"tokenizer_class": "DebertaV2Tokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]",
"vocab_type": "spm"
}
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