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 +2 -1
extract_config.json
CHANGED
|
@@ -11,6 +11,7 @@
|
|
| 11 |
"max_fields_per_pass": 16,
|
| 12 |
"null_threshold": {
|
| 13 |
"en": 0.4,
|
| 14 |
-
"vi": 0.8
|
|
|
|
| 15 |
}
|
| 16 |
}
|
|
|
|
| 11 |
"max_fields_per_pass": 16,
|
| 12 |
"null_threshold": {
|
| 13 |
"en": 0.4,
|
| 14 |
+
"vi": 0.8,
|
| 15 |
+
"default": 0.8
|
| 16 |
}
|
| 17 |
}
|