Instructions to use fimu-docproc-research/CIVQA_layoutXLM_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fimu-docproc-research/CIVQA_layoutXLM_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="fimu-docproc-research/CIVQA_layoutXLM_model")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("fimu-docproc-research/CIVQA_layoutXLM_model") model = AutoModelForDocumentQuestionAnswering.from_pretrained("fimu-docproc-research/CIVQA_layoutXLM_model") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,6 +1,7 @@
|
|
| 1 |
# The finetuned LayoutXLm model on Czech dataset for Visual Question Answering
|
| 2 |
|
| 3 |
The original model can be found [here](microsoft/layoutxlm-base)
|
|
|
|
| 4 |
The CIVQA dataset is the Czech Invoice dataset for Visual Question Answering
|
| 5 |
|
| 6 |
Achieved results:
|
|
|
|
| 1 |
# The finetuned LayoutXLm model on Czech dataset for Visual Question Answering
|
| 2 |
|
| 3 |
The original model can be found [here](microsoft/layoutxlm-base)
|
| 4 |
+
|
| 5 |
The CIVQA dataset is the Czech Invoice dataset for Visual Question Answering
|
| 6 |
|
| 7 |
Achieved results:
|