Joshua Herman zitterbewegung

## tldr-with-huggingface.ipynb

      
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                zitterbewegung
                / tldr-with-huggingface.ipynb
            
            
              Created
              September 14, 2020 02:21
            
              
                TLDR with Huggingface
              
          
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## japanese-dialogpt2-datacleaning.ipynb

      
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                zitterbewegung
                / japanese-dialogpt2-datacleaning.ipynb
            
            
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              August 10, 2020 13:27
            
              
                ipynb data processing
              
          
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## data_cleaning.ipynb

      
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                zitterbewegung
                / data_cleaning.ipynb
            
            
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              August 9, 2020 02:36
            
              
                Dialog with a gpt-2
              
          
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## requirements.txt
absl-py==0.9.0
appdirs==1.4.4
argcomplete==1.10.3
arrow==0.15.4
astor==0.8.1
beautifulsoup4==4.9.1
binaryornot==0.4.4
certifi==2017.7.27.1
chardet==3.0.4
clarifai==2.0.32

## sentiment140-fine-tuning-gpt-2-1-5b-using-tpus.ipynb

      
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                zitterbewegung
                / sentiment140-fine-tuning-gpt-2-1-5b-using-tpus.ipynb
            
            
              Created
              November 7, 2019 00:30
            
              
                Sentiment140 Fine-tuning GPT-2 1.5B using TPUs.ipynb
              
          
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## data_processing.py

import pandas as pd
data = pd.read_csv("/home/ec2-user/SageMaker/gpt-2-simple/gpt_2_simple/src/training.1600000.processed.noemoticon.csv",
                   encoding='latin-1',
                   names=["Label",
                          "tweet_id",
                          "Date", "Query",
                          'Username',
                          'tweet'])
data['tweet'] = '<|startoftext|>' + data['tweet'].astype(str) + "<|endoftext|>"

## gpt-2-twitter.ipynb

      
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                zitterbewegung
                / gpt-2-twitter.ipynb
            
            
              Last active
              October 24, 2019 02:54
            
              
                Modified version of Train a GPT-2 Text-Generating Model w/ GPU For Free to work with AWS.
              
          
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## example_manifest_file.json
{
  "package_name": "prose",
  "requirements": [
    {"name" : "prose-resolver",
     "UUID" : "ed721f86-c8ae-4360-af40-8c4d0b9485bb",
     "hash" : "0c63a75b845e4f7d01107d852e4c2485c51a50aaaa94fc61995e71bbee983a2ac3713831264adb47fb6bd1e058d5f004",
     "author" : {"name":"zitterbewegung",
                 "public_key": "asdf"

     },

## unknotting_problem.py
import spherogram
def generate_random_table(crossing_number, num_components, table_size):
  """
   Requires spherogram library (pip install sphereogram).
  """
    problem_set = [spherogram.random_link(crossing_number, num_components=num_components, initial_map_gives_link=True, alternating=True) for x in range(1,table_size)]
    return problem_set
poly = [x.alexander_polynomial() for x in generate_random_table(10, 1, 10)]
poly

## ux.py
print(responsejson)
print("3rd Concept" +get_concept(responsejson, 2))
print("4rd Concept" +get_concept(responsejson, 3))
print("5th Concept" +get_concept(responsejson, 4))
resp = MessagingResponse()
resp.message("I think it might be one of the following:  " +
get_concept(responsejson, 0) + " or " +
get_concept(responsejson, 1) + " or " +
get_concept(responsejson, 2) + " or " +
get_concept(responsejson, 3))
	absl-py==0.9.0
	appdirs==1.4.4
	argcomplete==1.10.3
	arrow==0.15.4
	astor==0.8.1
	beautifulsoup4==4.9.1
	binaryornot==0.4.4
	certifi==2017.7.27.1
	chardet==3.0.4
	clarifai==2.0.32

	import pandas as pd
	data = pd.read_csv("/home/ec2-user/SageMaker/gpt-2-simple/gpt_2_simple/src/training.1600000.processed.noemoticon.csv",
	encoding='latin-1',
	names=["Label",
	"tweet_id",
	"Date", "Query",
	'Username',
	'tweet'])
	data['tweet'] = '<\|startoftext\|>' + data['tweet'].astype(str) + "<\|endoftext\|>"
	{
	"package_name": "prose",
	"requirements": [
	{"name" : "prose-resolver",
	"UUID" : "ed721f86-c8ae-4360-af40-8c4d0b9485bb",
	"hash" : "0c63a75b845e4f7d01107d852e4c2485c51a50aaaa94fc61995e71bbee983a2ac3713831264adb47fb6bd1e058d5f004",
	"author" : {"name":"zitterbewegung",
	"public_key": "asdf"

	},
	import spherogram
	def generate_random_table(crossing_number, num_components, table_size):
	"""
	Requires spherogram library (pip install sphereogram).
	"""
	problem_set = [spherogram.random_link(crossing_number, num_components=num_components, initial_map_gives_link=True, alternating=True) for x in range(1,table_size)]
	return problem_set
	poly = [x.alexander_polynomial() for x in generate_random_table(10, 1, 10)]
	poly
	print(responsejson)
	print("3rd Concept" +get_concept(responsejson, 2))
	print("4rd Concept" +get_concept(responsejson, 3))
	print("5th Concept" +get_concept(responsejson, 4))
	resp = MessagingResponse()
	resp.message("I think it might be one of the following: " +
	get_concept(responsejson, 0) + " or " +
	get_concept(responsejson, 1) + " or " +
	get_concept(responsejson, 2) + " or " +
	get_concept(responsejson, 3))