Instructions to use hf-internal-testing/tiny-random-LayoutLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LayoutLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LayoutLMModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LayoutLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2804062b1ab40036c01176b59633730d34d5e935aca2d621d043e05c1e71a9dd
- Size of remote file:
- 909 kB
- SHA256:
- d06a69b68f9a3558aef17ff34e471c82e10687f4613fd9cf148826648fe93466
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.