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
- Xet hash:
- 7f93f5ba1128a30448508fa091b6703adebfef2e3efebd001bf27608d854ea34
- Size of remote file:
- 59.9 MB
- SHA256:
- 505a4d4681c080f059e16d8c3ca5576571d92e013161431484b121db11131336
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