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Update README.md

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  1. README.md +17 -9
README.md CHANGED
@@ -3,7 +3,7 @@ language:
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  - en
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  - zh
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  license: apache-2.0
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- library_name: transformers
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  tags:
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  - multimodal
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  - vqa
@@ -30,14 +30,22 @@ model-index:
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  value: 85
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  pipeline_tag: text-generation
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  widget:
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- - text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy."
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- example_title: "Sentiment analysis"
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- - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..."
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- example_title: "Coreference resolution"
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- - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..."
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- example_title: "Logic puzzles"
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- - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..."
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- example_title: "Reading comprehension"
 
 
 
 
 
 
 
 
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  ---
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  ### Model Sources
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  You need to use separate code, audio, text, and natural language together with the model. Because the model will use separate word segmenters and vocabularies to achieve the best results when dealing with special cases.
 
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  - en
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  - zh
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  license: apache-2.0
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+ library_name: adapter-transformers
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  tags:
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  - multimodal
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  - vqa
 
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  value: 85
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  pipeline_tag: text-generation
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  widget:
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+ - text: >-
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+ Is this review positive or negative? Review: Best cast iron skillet you will
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+ ever buy.
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+ example_title: Sentiment analysis
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+ - text: >-
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+ Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
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+ He chose her because she had ...
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+ example_title: Coreference resolution
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+ - text: >-
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+ On a shelf, there are five books: a gray book, a red book, a purple book, a
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+ blue book, and a black book ...
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+ example_title: Logic puzzles
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+ - text: >-
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+ The two men running to become New York City's next mayor will face off in
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+ their first debate Wednesday night ...
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+ example_title: Reading comprehension
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  ---
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  ### Model Sources
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  You need to use separate code, audio, text, and natural language together with the model. Because the model will use separate word segmenters and vocabularies to achieve the best results when dealing with special cases.