Instructions to use PeanutCoding/Layoutlmlargetest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeanutCoding/Layoutlmlargetest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="PeanutCoding/Layoutlmlargetest")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("PeanutCoding/Layoutlmlargetest") model = AutoModelForTokenClassification.from_pretrained("PeanutCoding/Layoutlmlargetest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 502282100e5ee2db9d513176a1bebaa32a132e6655b8ea3512ffef91fc3bcb79
- Size of remote file:
- 1.36 GB
- SHA256:
- 56d8b43983d9f8bdee62c4ba17d24d78fc0326ae28e282946d463fa64f40b414
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