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Docs(README): Updated inference example

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  1. README.md +30 -34
README.md CHANGED
@@ -1,36 +1,36 @@
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  ---
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  language:
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- - en
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  license: apache-2.0
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  tags:
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- - united states air force
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- - united states space force
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- - department of defense
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- - dod
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- - usaf
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- - ussf
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- - afi
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- - air force
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- - space force
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- - bullets
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- - performance reports
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- - evaluations
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- - awards
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- - opr
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- - epr
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- - narratives
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- - interpreter
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- - translation
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- - t5
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- - mbzuai
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- - lamini-flan-t5-783m
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- - flan-t5
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- - google
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- - opera
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- - justinthelaw
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  widget:
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- - text: "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context that is not already included in the Air and Space Force bullet statement: - Attended 4-hour EPD Instructor training; taught 3 2-hour Wing EPD & 4 1-hour bullet writing courses--prepared 164 for leadership"
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- example_title: "Example Usage"
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  ---
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  # Opera Bullet Interpreter
@@ -84,7 +84,7 @@ Used to programmatically produce training data for Opera's Bullet Forge (see
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  The exact prompt to achieve the desired result is:
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  ```
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- Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context that is not already included in the Air and Space Force bullet statement: <INSERT BULLET HERE>"
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  ```
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  ## Downstream Use
@@ -200,11 +200,7 @@ Use the code below to get started with the model.
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  import torch
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  from transformers import T5ForConditionalGeneration, T5Tokenizer
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- bullet_data_creation_prefix = (
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- "Using full sentences, expand upon the following Air and Space Force bullet statement by "
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- + "spelling-out acronyms and adding additional context that is not already included in the " +
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- "Air and Space Force bullet statement: "
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- )
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  # Path of the pre-trained model that will be used
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  model_path = "justinthelaw/opera-bullet-interpreter"
 
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  ---
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  language:
3
+ - en
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  license: apache-2.0
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  tags:
6
+ - united states air force
7
+ - united states space force
8
+ - department of defense
9
+ - dod
10
+ - usaf
11
+ - ussf
12
+ - afi
13
+ - air force
14
+ - space force
15
+ - bullets
16
+ - performance reports
17
+ - evaluations
18
+ - awards
19
+ - opr
20
+ - epr
21
+ - narratives
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+ - interpreter
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+ - translation
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+ - t5
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+ - mbzuai
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+ - lamini-flan-t5-783m
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+ - flan-t5
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+ - google
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+ - opera
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+ - justinthelaw
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  widget:
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+ - text: "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: - Attended 4-hour EPD Instructor training; taught 3 2-hour Wing EPD & 4 1-hour bullet writing courses--prepared 164 for leadership"
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+ example_title: "Example Usage"
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  ---
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  # Opera Bullet Interpreter
 
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  The exact prompt to achieve the desired result is:
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  ```
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+ Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: <INSERT BULLET HERE>"
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  ```
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  ## Downstream Use
 
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  import torch
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  from transformers import T5ForConditionalGeneration, T5Tokenizer
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+ bullet_data_creation_prefix = "Using full sentences, expand upon the following Air and Space Force bullet statement by spelling-out acronyms and adding additional context: "
 
 
 
 
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  # Path of the pre-trained model that will be used
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  model_path = "justinthelaw/opera-bullet-interpreter"