Instructions to use microsoft/layoutlmv3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/layoutlmv3-large with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/layoutlmv3-large", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Christoffer Koo Øhrstrøm commited on
Commit ·
95d7ee8
1
Parent(s): 0f239ba
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart.
Maximum crossload output difference=0.000e+00; Maximum crossload hidden layer difference=2.594e-04;
Maximum conversion output difference=0.000e+00; Maximum conversion hidden layer difference=2.594e-04;
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:e9df2f4967eb29213f9921781cf44051aa5d464d69ca53d037b5462d2ebc4b00
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size 1424583296
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