Object Detection
Transformers
PyTorch
TensorBoard
layoutlm
token-classification
Generated from Trainer
endpoints-template
Instructions to use Narsil/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Narsil/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Narsil/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Narsil/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 199 Bytes
-
https://huggingface.co/Narsil/layoutlm-funsd/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://Narsil/layoutlm-funsd@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Narsil/layoutlm-funsd/resolve/refs%2Fpr%2F1/preprocessor_config.json
199 Bytes
| { | |
| "apply_ocr": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv2FeatureExtractor", | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv2Processor", | |
| "resample": 2, | |
| "size": 224 | |
| } | |