MichelNivard

## The problem with (personal) non-ordinality:

n <- 30000 # 10k fictional people

# a pair of exposures, no measurmennt issues:
xa <- rnorm(n)
xb <- rnorm(n)

# Personal threshold 1 - 4 for each person, 
# reasonable scale design I think by which I mean the bins fill up sort of "normal" like this matters a lot!!

MichelNivard

require(MASS)

# fixed a2 and e2 for the entire script:
a <- .87 # additive genetic variance
e <- .13 # environmental variance

# make ZM covariance, i.e. the cov is a, the var = 1
sigma_mz <- matrix(c(1,a,a,1),2,2) 

# sample size (is big becuase rare traits)

MichelNivard

MichelNivard

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('./results')

# Set the device to GPU if available, otherwise use CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

MichelNivard

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments

# Set the path to the text file to fine-tune on
path_to_file = "path/to/text/file.txt"

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

MichelNivard

Speaker 0:

You wrote a piece a follow-up piece to your oral history titled, there is no replacement for black Twitter. I think back in November, What do you think we lose if we lose black Twitter? Tell

Speaker 1:

me not to meet your Mac, but we lose everything. I'm John Favreau. Welcome to offline.

Speaker 0:

MichelNivard

cat author_manuscript_txt.incr.2022-12-19/*/*.txt > merged-file.txt 



from datasets import load_dataset
dataset = load_dataset('text', data_files="merged-file.txt")
print(dataset)
dataset2 = dataset.filter(lambda x: len(x["text"]) > 500)
print(dataset2)

MichelNivard

from transformers import pipeline
from transformers import GPTJForCausalLM
from transformers import GPTJForCausalLM, AutoTokenizer
import torch

model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")

prompt = (
     "Monday Diary Entry: Had a busy weekend and started the day tired and empty, the good weather helps lift the mood. Worked fro home and spend (too much) time on learning about language models. Had 2 or 3 productive calls, tired and prob still a bit sick today, which put me in a somewhat somber mood. Had a long bath which maybe helped?"

MichelNivard

import tkinter
import customtkinter
from bs4 import BeautifulSoup

# Langchain loads:
from langchain.document_loaders import DirectoryLoader,PagedPDFSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS, Qdrant
from langchain.chains.qa_with_sources import load_qa_with_sources_chain

MichelNivard

Sys.setenv( # get an API key here: https://platform.openai.com/account/api-keys
  OPENAI_API_KEY = 'YOUR_API_KEY_HERE'
)


### Make a text "database" to search:
library(tm)
library(dplyr)
library(corpus)
library(rjson)