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 train_split.csv from neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/resolve/main/train_split.csv
- Command line
-
hf download hf://neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/train_split.csv
-
curl -L -o train_split.csv https://huggingface.co/neuralit/layoutlmv3-large-model2a-router-2aa-vs-2ab/resolve/main/train_split.csv
17.5 MB
- Xet hash:
- 4f927d7fffa174df349919d404c03ef4b5087d221cbf6c6e61540dbde42dd983
- Size of remote file:
- 17.5 MB
- SHA256:
- f5add8939dd5d240b18bbb3c3336efae4a00c6363ff66d87f66aa80d218bd15b
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