SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[LayoutLM] Add clarification to docs (#18716)

Browse Source 
* Add clarification

* Add another clarification

* Apply suggestion

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-09-02 14:48:19 +02:00
committed by GitHub
parent 129d73294e
commit c60dd98e87
3 changed files with 6 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
docs/source/en/model_doc/layoutlm.mdx
View File

@@ -67,7 +67,8 @@ occurs. Those can be obtained using the Python Image Library (PIL) library for e
```python ```python
from PIL import Image from PIL import Image
image = Image.open("name_of_your_document - can be a png file, pdf, etc.") # Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert("RGB")
width, height = image.size width, height = image.size
``` ```

3
src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py
View File

@@ -168,7 +168,8 @@ class LayoutLMv2FeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionM
>>> from transformers import LayoutLMv2FeatureExtractor >>> from transformers import LayoutLMv2FeatureExtractor
>>> from PIL import Image >>> from PIL import Image
>>> image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB") >>> # Document can be a png, jpg, etc. PDFs must be converted to images.
>>> image = Image.open(name_of_your_document).convert("RGB")
>>> # option 1: with apply_ocr=True (default) >>> # option 1: with apply_ocr=True (default)
>>> feature_extractor = LayoutLMv2FeatureExtractor() >>> feature_extractor = LayoutLMv2FeatureExtractor()

3
src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py
View File

@@ -179,7 +179,8 @@ class LayoutLMv3FeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionM
>>> from transformers import LayoutLMv3FeatureExtractor >>> from transformers import LayoutLMv3FeatureExtractor
>>> from PIL import Image >>> from PIL import Image
>>> image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB") >>> # Document can be a png, jpg, etc. PDFs must be converted to images.
>>> image = Image.open(name_of_your_document).convert("RGB")
>>> # option 1: with apply_ocr=True (default) >>> # option 1: with apply_ocr=True (default)
>>> feature_extractor = LayoutLMv3FeatureExtractor() >>> feature_extractor = LayoutLMv3FeatureExtractor()