Instructions to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 459 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification/resolve/96515f699874cfbfbec7a64c539ae92419e4c6dc/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification@96515f699874cfbfbec7a64c539ae92419e4c6dc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMv3ForSequenceClassification/resolve/96515f699874cfbfbec7a64c539ae92419e4c6dc/pytorch_model.bin
459 kB
- Xet hash:
- 9a909aea7e5fe42d6072eb7dcae4e4b574a31c1e7d8644f2b00b6d364ab22bd1
- Size of remote file:
- 459 kB
- SHA256:
- c57416f04e135e1e667a2fb6f2f05295097dea17cb19fb1f5356891fe497a321
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