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
Add discovery tags
Browse files
README.md
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pipeline_tag: token-classification
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library_name: transformers
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tags:
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- information-extraction
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- structured-output
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- gemma4
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---
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pipeline_tag: token-classification
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library_name: transformers
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tags:
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- systemone
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- extraction-model
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- text-to-typed-output
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- information-extraction
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- key-information-extraction
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- extractive-qa
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- span-extraction
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- structured-output
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- document-ai
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- multilingual
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- gemma4
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