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
Download extract_config.json from beyoru/gemmaExtract: direct link, hf CLI and curl.
- Browser
- Download file 250 Bytes
-
https://huggingface.co/beyoru/gemmaExtract/resolve/main/extract_config.json
- Command line
-
hf download hf://beyoru/gemmaExtract/extract_config.json
-
curl -L -o extract_config.json https://huggingface.co/beyoru/gemmaExtract/resolve/main/extract_config.json
250 Bytes
| { | |
| "hidden": 2560, | |
| "head": { | |
| "width": 1024, | |
| "routing": 2, | |
| "layers": 4, | |
| "heads": 16, | |
| "ff": 4096 | |
| }, | |
| "max_length": 2048, | |
| "max_fields_per_pass": 16, | |
| "null_threshold": { | |
| "en": 0.4, | |
| "vi": 0.8, | |
| "default": 0.8 | |
| } | |
| } |