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Start dataset creation.

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Files changed (4) hide show
  1. data.py +2 -0
  2. data_set_training.csv +119 -0
  3. system_promts.txt +9 -0
  4. train.py +4 -0
data.py CHANGED
@@ -2,6 +2,8 @@ from transformers import AlbertTokenizer, AlbertModel
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  from sklearn.metrics.pairwise import cosine_similarity
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  import torch
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  # base
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  # large
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  tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
 
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  from sklearn.metrics.pairwise import cosine_similarity
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  import torch
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+ #This is a quick evaluation to see if B
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+
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  # base
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  # large
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  tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
data_set_training.csv ADDED
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+ 101 Crawfords Corner Road, Suite 2511-W, Holmdel NJ 07734|101 Crawfords Corner Rd, Ste 2511-W, Holmdel NJ 07734|1
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+ 101 Crawfords Corner Road, Suite 2511-W, Holmdel NJ 07734|101 Crawfords Corner Rd, Suite 2511-W, Holmdel NJ 07734|1
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+ 777 Pine Street, Suite 100, Portland, 90123|2345 Birch Avenue, Suite 50, Manchester, 23456|0
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+ 1234 Birch Court, Suite 50, Manchester, 23456|1234 Cedar Lane, Suite 50, Oceanview, 23456|0
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+ 2178 Oak Road, Suite 101, Greenfield, 45678|3456 Maple Boulevard, Suite 200, Sunnyville, 56789|0
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+ 345 Park Lane, Suite 75, Richmond, 23456|7890 Willow Road, Suite 300, Greenville, 89012|0
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+ 5678 Grove Boulevard, Suite 150, Savannah, 34567|6789 Pine Street, Suite 100, Portland, 90123|0
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system_promts.txt ADDED
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+ Create similar addresses:
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+ ----------------------------
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+ You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 variations of the provided address as a regular person may enter it into some system. All outputs should be a list of 10 sample.
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+
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+ Create similar but different addresses:
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+ You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 variations of the provided address that are actually different addresses. Address formats should be as if a regular person may enter it into some system.
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+
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+ Generate completely different addresses.
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+ You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 random addresses that are inspired by the structure of the address user provided. Address formats should be as if a regular person may enter it into some system.
train.py ADDED
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+ # Will be based on
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+ # ConstructiveLoss function.
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+ #
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+ # https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/quora_duplicate_questions/training_OnlineContrastiveLoss.py