veekaybee

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

Pre-Transformer Models

georgwiese

""" Enhancing Intent Classification with the Universal Sentence Encoder:
https://medium.com/scalableminds/enhancing-intent-classification-with-the-universal-sentence-encoder-ecbcd7a3005c
"""

from rasa_nlu.featurizers import Featurizer

import tensorflow_hub as hub
import tensorflow as tf

W4ngatang

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

bogsio

# https://nlpforhackers.io/named-entity-extraction/

import os
import string
import collections
import pickle
from collections import Iterable

from nltk.tag import ClassifierBasedTagger
from nltk.chunk import ChunkParserI, conlltags2tree, tree2conlltags

codekansas

from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge