Text Classification
Transformers
Safetensors
layoutlmv3
document-classification
medical-documents
model2a
Generated from Trainer
Instructions to use neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab") model = AutoModelForSequenceClassification.from_pretrained("neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bafdb64d4d66080c79a55a2f80fb5d7e21f2a60e58ea192e093a1018cd4bff8
- Size of remote file:
- 19.4 MB
- SHA256:
- 1ba29dd70fe7522c1151bc85aa156a1fa867299ea4368ec00df7f56a17cad146
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.