Instructions to use philschmid/lilt-en-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/lilt-en-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="philschmid/lilt-en-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/lilt-en-funsd") model = AutoModelForTokenClassification.from_pretrained("philschmid/lilt-en-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from philschmid/lilt-en-funsd: direct link, hf CLI and curl.
- Browser
- Download file 346 Bytes
-
https://huggingface.co/philschmid/lilt-en-funsd/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://philschmid/lilt-en-funsd/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/philschmid/lilt-en-funsd/resolve/main/preprocessor_config.json
346 Bytes
| { | |
| "apply_ocr": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv3FeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv3Processor", | |
| "resample": 2, | |
| "size": 224, | |
| "tesseract_config": "" | |
| } | |