Instructions to use Sharka/CIVQA_Impira_QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_Impira_QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_Impira_QA")# Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sharka/CIVQA_Impira_QA") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_Impira_QA") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
---
|
| 4 |
-
# Impira Document QA Model Fine-tuned with CIVQA dataset
|
| 5 |
|
| 6 |
This is a fine-tuned version of the [Impira model](https://huggingface.co/impira/layoutlm-document-qa), which was trained on Czech Invoice Visual Question Answering (CIVQA) datasets containing invoices in the Czech language.
|
| 7 |
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
---
|
| 4 |
+
# Impira Document QA Model Fine-tuned with CIVQA (Tesseract) dataset
|
| 5 |
|
| 6 |
This is a fine-tuned version of the [Impira model](https://huggingface.co/impira/layoutlm-document-qa), which was trained on Czech Invoice Visual Question Answering (CIVQA) datasets containing invoices in the Czech language.
|
| 7 |
|