Image-Text-to-Text
PaddleOCR
Safetensors
English
Chinese
multilingual
paddleocr_vl
ERNIE4.5
PaddlePaddle
image-to-text
ocr
document-parse
layout
table
formula
chart
conversational
custom_code
Eval Results
Instructions to use PaddlePaddle/PaddleOCR-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use PaddlePaddle/PaddleOCR-VL with PaddleOCR:
# See https://www.paddleocr.ai/latest/version3.x/pipeline_usage/PaddleOCR-VL.html to installation from paddleocr import PaddleOCRVL pipeline = PaddleOCRVL(pipeline_version="v1") output = pipeline.predict("path/to/document_image.png") for res in output: res.print() res.save_to_json(save_path="output") res.save_to_markdown(save_path="output") - Notebooks
- Google Colab
- Kaggle
zhangyue66 commited on
Commit ·
d7d1f37
1
Parent(s): 4fe79f6
update
Browse files
README.md
CHANGED
|
@@ -200,12 +200,12 @@ print(outputs)
|
|
| 200 |
<details>
|
| 201 |
<summary>👉 Click to expand: Use flash-attn to boost performance and reduce memory usage</summary>
|
| 202 |
|
| 203 |
-
|
| 204 |
# ensure the flash-attn2 is installed
|
| 205 |
pip install flash-attn --no-build-isolation
|
| 206 |
-
|
| 207 |
|
| 208 |
-
|
| 209 |
import torch
|
| 210 |
from transformers import AutoModelForCausalLM, AutoProcessor
|
| 211 |
from PIL import Image
|
|
@@ -260,7 +260,7 @@ with torch.inference_mode():
|
|
| 260 |
|
| 261 |
outputs = processor.batch_decode(out, skip_special_tokens=True)[0]
|
| 262 |
print(outputs)
|
| 263 |
-
|
| 264 |
|
| 265 |
</details>
|
| 266 |
|
|
|
|
| 200 |
<details>
|
| 201 |
<summary>👉 Click to expand: Use flash-attn to boost performance and reduce memory usage</summary>
|
| 202 |
|
| 203 |
+
```shell
|
| 204 |
# ensure the flash-attn2 is installed
|
| 205 |
pip install flash-attn --no-build-isolation
|
| 206 |
+
```
|
| 207 |
|
| 208 |
+
```python
|
| 209 |
import torch
|
| 210 |
from transformers import AutoModelForCausalLM, AutoProcessor
|
| 211 |
from PIL import Image
|
|
|
|
| 260 |
|
| 261 |
outputs = processor.batch_decode(out, skip_special_tokens=True)[0]
|
| 262 |
print(outputs)
|
| 263 |
+
```
|
| 264 |
|
| 265 |
</details>
|
| 266 |
|