Dan Ofer ddofer

download model

if [[ ! -e 'numberbatch-17.06.txt' ]]; then
    wget https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-17.06.txt.gz
    gunzip numberbatch-17.06.txt.gz
fi
sudo pip install wordfreq
sudo pip install gensim

Get my forked tensorflow graphics repo and switch to appropriate branch

git clone https://github.com/jackd/graphics.git
cd graphics
git checkout sparse-feastnet
pip install -e .
cd ..

Get this gist:

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

	class AttentionWithContext(Layer):
	"""
	Attention operation, with a context/query vector, for temporal data.
	Supports Masking.
	Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
	"Hierarchical Attention Networks for Document Classification"
	by using a context vector to assist the attention
	# Input shape
	3D tensor with shape: `(samples, steps, features)`.
	# Output shape

	# This is not presently all encompassing as it was started well after my sequence work repo
	# at https://github.com/fomightez/sequencework , where much of this related code is.



	# For making FASTA files/entriees out of dataframes, see 'specific dataframe contents saved as formatted text file example'
	# in my useful pandas snippets gist https://gist.github.com/fomightez/ef57387b5d23106fabd4e02dab6819b4

	"""
	A scikit-learn like transformer to remove a confounding effect on X.
	"""

	from sklearn.base import BaseEstimator, TransformerMixin, clone
	from sklearn.linear_model import LinearRegression
	import numpy as np

	class DeConfounder(BaseEstimator, TransformerMixin):
	""" A transformer removing the effect of y on X.

	# how to use : df should be the dataframe restricted to categorical values to impact,
	# target should be the pd.series of target values.
	# Use fit, transform etc.
	# three types : binary, multiple, continuous.
	# for now m is a param <===== but what should we put here ? I guess some function of total shape.
	# I mean what would be the value of m we want to have for 0.5 ?

	import pandas as pd
	import numpy as np

	def describe_robust(self, percentiles=None, include=None, exclude=None, trim=0.2):
	"""
	Monkey-patch for pd.Dataframe.describe based on robust statistics.
	Calculate trimmed mean and winsorized standard deviation with default trim 0.2.
	Uses scipy.stats.mstats (trimmed_mean, winsorized) and numpy.std

	See e.g. http://www.uh.edu/~ttian/ES.pdf for methodical background.

	BSD 3-Clause License