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")# pip install -U transformers accelerate # 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
Download selected_training_rows.csv from neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab: direct link, hf CLI and curl.
- Browser
- Download file 19.4 MB
-
https://huggingface.co/neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/resolve/main/selected_training_rows.csv
- Command line
-
hf download hf://neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/selected_training_rows.csv
-
curl -L -o selected_training_rows.csv https://huggingface.co/neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/resolve/main/selected_training_rows.csv
19.4 MB
- Xet hash:
- 6bafdb64d4d66080c79a55a2f80fb5d7e21f2a60e58ea192e093a1018cd4bff8
- Size of remote file:
- 19.4 MB
- SHA256:
- 1ba29dd70fe7522c1151bc85aa156a1fa867299ea4368ec00df7f56a17cad146
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