Token Classification
Transformers
Safetensors
English
Vietnamese
gemma4
image-text-to-text
systemone
extraction-model
text-to-typed-output
information-extraction
key-information-extraction
extractive-qa
span-extraction
structured-output
document-ai
multilingual
Instructions to use beyoru/gemmaExtract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beyoru/gemmaExtract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="beyoru/gemmaExtract")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("beyoru/gemmaExtract") model = AutoModelForMultimodalLM.from_pretrained("beyoru/gemmaExtract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload extract_config.json with huggingface_hub
Browse files- extract_config.json +16 -0
extract_config.json
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{
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"hidden": 2560,
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"head": {
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"width": 1024,
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"routing": 2,
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"layers": 4,
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"heads": 16,
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"ff": 4096
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},
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"max_length": 2048,
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"max_fields_per_pass": 16,
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"null_threshold": {
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"en": 0.4,
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"vi": 0.8
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}
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}
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