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Kevin Prybol kprybol

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Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:
0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach
@tamuhey
tamuhey / tokenizations_post.md
Last active July 27, 2024 14:46
How to calculate the alignment between BERT and spaCy tokens effectively and robustly

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

image

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

@abhishekkrthakur
abhishekkrthakur / slack_notifier.py
Created December 6, 2019 07:53
Slack notification from python
import os
import requests
import json
SLACK_WEBHOOK= os.environ.get("SLACK_WEBHOOK")
def send_message(messages, channel="abhishek", username="beast"):
"""
:param messages: list of texts
@jaradc
jaradc / entropy_calculation_in_python.py
Last active April 3, 2024 18:16
Four different ways to calculate entropy in Python
import numpy as np
from scipy.stats import entropy
from math import log, e
import pandas as pd
import timeit
def entropy1(labels, base=None):
value,counts = np.unique(labels, return_counts=True)
return entropy(counts, base=base)
@nigeljyng
nigeljyng / TemporalMaxPooling.py
Last active February 21, 2020 15:56
Temporal max pooling as implemented in https://arxiv.org/abs/1511.04108
from keras import backend as K
from keras.engine import InputSpec
from keras.engine.topology import Layer
import numpy as np
class TemporalMaxPooling(Layer):
"""
This pooling layer accepts the temporal sequence output by a recurrent layer
and performs temporal pooling, looking at only the non-masked portion of the sequence.
@empet
empet / Plotly-Julia-Set.ipynb
Last active July 4, 2016 14:04
Escape time algorithm to get Plotly plot of a Julia set
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@dmrd
dmrd / IntroToJulia.ipynb
Created April 29, 2016 01:14
Notebook walking through a few Julia examples
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