Instructions to use TusharGoel/LayoutLM-Finetuned-DocVQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TusharGoel/LayoutLM-Finetuned-DocVQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="TusharGoel/LayoutLM-Finetuned-DocVQA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("TusharGoel/LayoutLM-Finetuned-DocVQA") model = AutoModelForDocumentQuestionAnswering.from_pretrained("TusharGoel/LayoutLM-Finetuned-DocVQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
cecb128
1
Parent(s): c321621
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,7 +8,8 @@ This model trained on DocVQA Dataset on sample of 15000 questions
|
|
| 8 |
|
| 9 |
Code for Training and Prediction (v1): https://www.kaggle.com/tusharcode/training-layoutlm-docvqa
|
| 10 |
|
| 11 |
-
|
|
|
|
| 12 |
from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering
|
| 13 |
from datasets import load_dataset
|
| 14 |
|
|
|
|
| 8 |
|
| 9 |
Code for Training and Prediction (v1): https://www.kaggle.com/tusharcode/training-layoutlm-docvqa
|
| 10 |
|
| 11 |
+
**How to use:**
|
| 12 |
+
```python
|
| 13 |
from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering
|
| 14 |
from datasets import load_dataset
|
| 15 |
|