Instructions to use hf-internal-testing/tiny-random-LayoutLMv2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMv2ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "apply_ocr": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv2FeatureExtractor", | |
| "image_processor_type": "LayoutLMv2ImageProcessor", | |
| "ocr_lang": null, | |
| "resample": 2, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |