mapmeld

• In re Anastaplo: the Red Scare and the Supreme Court
• Cohen v. Hurley (people get this one wrong) (lawyer 5th)
• Petitions from jail / Warren Court / recent case (Wooden)
• Women pro se at the Supreme Court
• Phillips (it happened twice??)
• Soldier Schlanger
• "You have a right to be an attorney, if you want to"
• Griffin v Griffin, + Divorce at the Supreme Court
• Competency case
• Sloan and other attempts, Flynt audio

mapmeld

# retrieve contig
searchables = ['JAWIZW010000099.1', 'JAWIZW010000077.1']
contigs = []
with open('./hxntes/ncbi_dataset/data/GCA_034703905.1/GCA_034703905.1_C.berlandieri_1.0_genomic.fna', 'r') as contigfile:
    inline = False
    sequence = ""
    lastSection = ""
    for line in contigfile:
        if '>' in line:

mapmeld

import json

j = json.load(open('./census-reviewed.json', 'r'))
headers = None
total_vars = {
    'P1_047N': 0,
    'P1_063N': 0,
    'P1_070N': 0,
    'P2_072N': 0,
    'Hisp': 0,

mapmeld

# this should run on a GPU CoLab notebook
# pip install langchain xformers transformers datasets bitsandbytes accelerate --quiet
# get access to the meta-llama models, accept license, and get a read token

hf_auth = '######'

from langchain.chains import ConversationChain
from langchain.llms import HuggingFacePipeline
from langchain.memory import ConversationSummaryBufferMemory
from langchain.prompts.prompt import PromptTemplate

mapmeld

累令直漢刃

累令直漢刃

mapmeld

Date: February 25, 2023

Questions in quotes

My comments in bold italics

Hi, I'm going to ask some questions about New York City as a new visitor, and you should respond as an expert resident.

Sure, I'm happy to help! What would you like to know about New York City?

mapmeld

# All I'm looking for on an ML example:
# ! pip install name_of_library

from name_of_library import model, other_stuff

tdata = load_data_from_file() # not a built-in datasets source where I'd need to write python to add data
tdata.apply(changes) # whose dataset is so perfect we don't edit it

model.train(tdata, **explained_params)

mapmeld

Patching Models with New Words, People, and Events

May 6 - June 15, 2021

Scope

Once a large pre-trained language model is published, it is a snapshot of language when its corpus was collected. What are ways to update models to support new or newly-frequent terms (BIPOC), phrasing (social distancing), or people and events (Fyre Festival)? What are reliable, low-cost ways to test and benchmark these methods of updating?

Current status

mapmeld

/*
  Generally, don't run random JS in your browser console, especially on financial sites, but here we are
  By default this sorts by Percent Change. If you uncomment the next line it sorts by myDelta (price x your shares)
  Caveats:
  - I'm not affiliated with Vanguard or any licensed financial advisor or tax preparer. I don't have a clue what's going on with your finances.
  - The script assumes you did NOT trade today; it uses today's change and current shares
  - Delta-sort does not handle penny stocks as well because the UI says 0.01 and we reverse-engineer from current balance
*/

let sortRule = 'pct';

mapmeld

t5.data.TaskRegistry.add(
      "byt5_ex",
      t5.data.TextLineTask,
      split_to_filepattern={
            "train": "gs://BUCKET/train_lines.txt",
            "validation": "gs://BUCKET/validation_lines.txt",
        },
      text_preprocessor=[
        functools.partial(
          t5.data.preprocessors.parse_tsv,