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Doc styler examples (#14953)

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* Fix bad examples

* Add black formatting to style_doc

* Use first nonempty line

* Put it at the right place

* Don't add spaces to empty lines

* Better templates

* Deal with triple quotes in docstrings

* Result of style_doc

* Enable mdx treatment and fix code examples in MDXs

* Result of doc styler on doc source files

* Last fixes

* Break copy from
This commit is contained in:
Sylvain Gugger
2021-12-27 19:07:46 -05:00
committed by GitHub
parent e13f72fbff
commit b5e2b183af
211 changed files with 2738 additions and 1711 deletions
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28
docs/source/model_doc/layoutlmv2.mdx
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@@ -70,12 +70,12 @@ Tips:
```python
def normalize_bbox(bbox, width, height):
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
```
Here, `width` and `height` correspond to the width and height of the original document in which the token
@@ -123,7 +123,7 @@ modality.
```python
from transformers import LayoutLMv2FeatureExtractor, LayoutLMv2TokenizerFast, LayoutLMv2Processor
feature_extractor = LayoutLMv2FeatureExtractor() # apply_ocr is set to True by default
feature_extractor = LayoutLMv2FeatureExtractor() # apply_ocr is set to True by default
tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")
processor = LayoutLMv2Processor(feature_extractor, tokenizer)
```
@@ -158,7 +158,9 @@ from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
encoding = processor(image, return_tensors="pt") # you can also add all tokenizer parameters here such as padding, truncation
encoding = processor(
image, return_tensors="pt"
) # you can also add all tokenizer parameters here such as padding, truncation
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
```
@@ -177,7 +179,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
encoding = processor(image, words, boxes=boxes, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
@@ -199,7 +201,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
word_labels = [1, 2]
encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
print(encoding.keys())
@@ -219,7 +221,7 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
question = "What's his name?"
encoding = processor(image, question, return_tensors="pt")
encoding = processor(image, question, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
```
@@ -238,8 +240,8 @@ processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncas
image = Image.open("name_of_your_document - can be a png file, pdf, etc.").convert("RGB")
question = "What's his name?"
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # make sure to normalize your bounding boxes
encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
```