#text prediction

[pythonprogrammingsnippets]

python iterative monte carlo search for text generation using nltk

You are playing a game and you want to win. But you don't know what move to make next, because you don't know what the other player will do. So, you decide to try different moves randomly and see what happens. You repeat this process again and again, each time learning from the result of the move you made. This is called iterative Monte Carlo search. It's like making random moves in a game and learning from the outcome each time until you find the best move to win.

Iterative Monte Carlo search is a technique used in AI to explore a large space of possible solutions to find the best ones. It can be applied to semantic synonym finding by randomly selecting synonyms, generating sentences, and analyzing their context to refine the selection.

# an iterative monte carlo search example using nltk # https://pythonprogrammingsnippets.tumblr.com import random from nltk.corpus import wordnet # Define a function to get the synonyms of a word using wordnet def get_synonyms(word): synonyms = [] for syn in wordnet.synsets(word): for l in syn.lemmas(): if '_' not in l.name(): synonyms.append(l.name()) return list(set(synonyms)) # Define a function to get a random variant of a word def get_random_variant(word): synonyms = get_synonyms(word) if len(synonyms) == 0: return word else: return random.choice(synonyms) # Define a function to get the score of a candidate sentence def get_score(candidate): return len(candidate) # Define a function to perform one iteration of the monte carlo search def monte_carlo_search(candidate): variants = [get_random_variant(word) for word in candidate.split()] max_candidate = ' '.join(variants) max_score = get_score(max_candidate) for i in range(100): variants = [get_random_variant(word) for word in candidate.split()] candidate = ' '.join(variants) score = get_score(candidate) if score > max_score: max_score = score max_candidate = candidate return max_candidate initial_candidate = "This is an example sentence." # Perform 10 iterations of the monte carlo search for i in range(10): initial_candidate = monte_carlo_search(initial_candidate) print(initial_candidate)

output:

This manufacture Associate_in_Nursing theoretical_account sentence. This fabricate Associate_in_Nursing theoretical_account sentence. This construct Associate_in_Nursing theoretical_account sentence. This cathode-ray_oscilloscope Associate_in_Nursing counteract sentence. This collapse Associate_in_Nursing computed_axial_tomography sentence. This waste_one's_time Associate_in_Nursing gossip sentence. This magnetic_inclination Associate_in_Nursing temptingness sentence. This magnetic_inclination Associate_in_Nursing conjure sentence. This magnetic_inclination Associate_in_Nursing controversy sentence. This inclination Associate_in_Nursing magnetic_inclination sentence.

[imsorryimlate]

when’s your next appointment for your new job and how much do you need to pay for it and how much is it gonna cost you to get it done and how much does it cost to get it fixed and how much money is it gonna be paid for it to be done and how much time do you need to get it out of the way to get it back to you and get it done and then you can get it done and then i can go back to work and get it done and then we can go to the bank and get it done and then we can go to the car wash and get it done and then go to the store

[clay-pidgeon]

writing a post using exclusively text prediction let’s go baby

i don’t know what to do with my life right now

WOAH THAT GOT SAD

[demigods-posts]

i think grover pointing out to percy that annabeth's yankee's cap is the only thing she has of her godly parent makes percy realize how important it is for demigods to have symbols of their parents with them. and i would really love to see it all come full circle when percy battles with ares later on in season one, and after he wins, he demands that ares leave them alone so they can return the bolt and that he gives clarisse another spear to make up for the one he broke. which would be an amazing way to set up percy and clarisse's 'i have a lot of respect for you/you're an annoying bitch that i tolerate/if anyone messes with you, i'll cut them' trope for later seasons.

[thequerysquad]

Everything You Need to Know About ChatGPT: A Comprehensive Overview of the Language Model

Please Follow my page for more latest Tech Info

Who is the founder of ChatGPT? ChatGPT is an open-source chatbot developed by OpenAI, a research organization that focuses on developing and promoting friendly artificial intelligence. OpenAI was founded in 2015 by a group of entrepreneurs, researchers, and philanthropists, including Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, and Wojciech Zaremba. The organization’s mission is to…

[beif0ngs]

Wednesday Addams + text post meme

[iwowzumi]

chuuya’s role in bsd is so funny to me. you’re watching this show and every 10 episodes or so this pretty guy drops in, and he’s the coolest most OP guy in the show so he can really only hang around for a few minutes before he makes any conflict obsolete. and he’s supposed to be part of the villains but he never really does anything that evil and is mostly helpful? he just appears and does something badass and fucks off again. and this is all already really weird but also every time he shows up it’s clear that he and one of the other main characters have like, definitely fucked, which adds a whole other layer of absurdism. and the best part is he accomplishes this in maybe 11 total minutes of screen time.

[reunitedinterlude]

a compilation (1, 2, 3)

bonus:

[thatwholethingwiththeduck]

predictive text

didn't even start typing but these are the words my phone thinks i might want to use

[arthropooda]

[pythonprogrammingsnippets]

understanding attention mechanisms in natural language processing

Attention mechanisms are used to help the model focus on the important parts of the input text when making predictions. There are different types of attention mechanisms, each with their own pros and cons. These are four types of attention mechanisms: Full self-attention, Sliding window attention, Dilated sliding window attention, and Global sliding window attention.

Full self-attention looks at every word in the input text, which can help capture long-range dependencies. However, it can be slow for long texts.

Sliding window attention only looks at a small chunk of the input text at a time, which makes it faster than full self-attention. However, it might not capture important information outside of the window.

Dilated sliding window attention is similar to sliding window attention, but it skips over some words in between the attended words. This makes it faster and helps capture longer dependencies, but it may still miss some important information.

Global sliding window attention looks at all the words in the input text but gives different weights to each word based on its distance from the current position. It's faster than full self-attention and can capture some long-range dependencies, but it might not be as good at capturing all dependencies.

The downsides of these mechanisms are that they may miss important information, which can lead to less accurate predictions. Additionally, choosing the right parameters for each mechanism can be challenging and require some trial and error.

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Let's say you have a sentence like "The cat sat on the mat." and you want to use a deep learning model to predict the next word in the sequence. With a basic model, the model would treat each word in the sentence equally and assign the same weight to each word when making its prediction.

However, with an attention mechanism, the model can focus more on the important parts of the sentence. For example, it might give more weight to the word "cat" because it's the subject of the sentence, and less weight to the word "the" because it's a common word that doesn't provide much information.

To do this, the model uses a scoring function to calculate a weight for each word in the sentence. The weight is based on how relevant the word is to the prediction task. The model then uses these weights to give more attention to the important parts of the sentence when making its prediction.

So, in this example, the model might use attention to focus on the word "cat" when predicting the next word in the sequence, because it's the most important word for understanding the meaning of the sentence. This can help the model make more accurate predictions and improve its overall performance in natural language processing tasks.

[sarah-cam]

it appears as though i am but a whore for silly little guys from silly little teen dramas that are the hot broody older brothers with green eyes that are just trying their best 🫡

[eatmy-customjorts]

biblically accurate terukane divorce

[demigods-posts]

all i'm saying is you can't tell me that when annabeth watched percy hold up medusa's head and kill alecto, she didn't immediately think of that statue in the new york metropolitan museum of arts of his namesake doing the exact same thing. you can't tell me she didn't recognize his courage and realize how powerful and essential this boy is going to be for the future of the world as they know it.

[tamarrud]

Israeli soldiers cheer on after blowing up a UN run school in Beit Hanoun, Northern Gaza (December 2023)

These are the same sickos who would sit on hilltops, snack and watch as bombs drop on Gaza in 2014 when over 2000 Palestinians were killed.

Despicable.

[abstractfrog]

My comic for The Retired Colourman! I love this scene between Mariana and Sherlock sm