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Translate en/model_doc to JP (#27264)

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* Add `model_docs`

* Add

* Update Model adoc

* Update docs/source/ja/model_doc/bark.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/ja/model_doc/beit.md

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* Update docs/source/ja/model_doc/bit.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/ja/model_doc/blenderbot.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/ja/model_doc/blenderbot-small.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* update reiew-1

* Update toctree.yml

* translating docs and fixes of PR #27401

* Update docs/source/ja/model_doc/bert.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/ja/model_doc/bert-generation.md

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* Update the model docs

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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2023-11-28 02:49:04 +05:30
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docs/source/ja/model_doc/align.md
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@@ -51,10 +51,10 @@ inputs = processor(text=candidate_labels, images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# これは画像-テキスト類似度スコア
# this is the image-text similarity score
logits_per_image = outputs.logits_per_image
# Softmaxを取ることで各ラベルの確率を得られる
# we can take the softmax to get the label probabilities
probs = logits_per_image.softmax(dim=1)
print(probs)
```