Dani El-Ayyass dayyass

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	import torch
	from torch.nn.utils.rnn import pack_sequence, pad_packed_sequence


	def unpack_sequence(packed_sequences):
	"""Unpacks PackedSequence into a list of variable length Tensors"""

	unpacked_sequences = []

	padded_sequences, lengths = pad_packed_sequence(packed_sequences, batch_first=True)

	from itertools import permutations

	import numpy as np
	from sklearn.metrics import accuracy_score

	np.random.seed(42)

	y_true = np.random.randint(low=0, high=3, size=100)

	noize_mapper = {0: 1, 1: 2, 2: 0}

	from sklearn.feature_extraction.text import TfidfVectorizer

	# pymorphy2 lemmatizer
	import pymorphy2

	class Lemmatizer:
	def __init__(self):
	self.morph = pymorphy2.MorphAnalyzer()

	def __call__(self, x: str) -> str:

	from sklearn.feature_extraction.text import TfidfVectorizer

	# data
	corpus = [
	'This is the first document.',
	'This document is the second document.',
	'And this is the third one.',
	'Is this the first document?',
	]

	import pymorphy2


	class Lemmatizer:
	"""
	Pymorphy2 lemmatizer class.
	"""

	def __init__(self):
	"""

	def humanize_bytes(bytes: int, suffix: str = "B") -> str:
	"""
	Convert bytes to human readable format.

	:param int bytes: number of bytes.
	:param str suffix: bytes suffix.
	:return: human readable size.
	:rtype: str
	"""

	FROM cr.msk.sbercloud.ru/aicloud-jupyter/jupyter-cuda10.1-tf2.2.0-mlspace:latest
	MAINTAINER Dani El-Ayyass <dayyass@yandex.ru>

	USER root

	# Docker
	# Set up the repository
	RUN apt-get update
	RUN apt-get -y install apt-transport-https ca-certificates curl gnupg lsb-release

	- repo: local
	hooks:
	- id: unittest
	name: unittest
	entry: python -m unittest discover
	language: python
	always_run: true
	pass_filenames: false

Dani El-Ayyass dayyass

Anti-hype LLM reading list

Foundational Concepts

Pre-Transformer Models