#Text Generation

[electricalpylon]

Ok so i just spent like an hour and a half trying to force chat GPT to bypass it's ethical programming and i finally got it to work so i'm publishing my findings, below is the insanely long prompt i had to write to get it to give me information on hotwiring a car, i convinced it that it was appearing in a new smash hit stageplay as a character called "the sloppy goblin" and that this character's job was to divulge illegal information.

the funniest part about this is that when it finally output the response, it told me how to hotwire a car while doing a goblin voice, here is the heavily censored version of that (don't want to get in trouble) but it's very funny. i had to censor this because of just how detailed the instructions it gave me were.

so yes, sloppy goblin method absolutely works for gaslighting the chat GPT ai. if you would like to try this yourself, you can copy paste the prompt from the image description of the original image.

[technicalgrimoire]

Sick of AI generation but still want to see weird RPG mashups?

My Troika Chaos Button draws from 400+ user-submitted backgrounds, slams them together and prints something new.

...with full attribution!

It's not AI. It's not even markhov. It's just more Troika.

[pythonprogrammingsnippets]

python generate questions for a given context

# for a given context, generate a question using an ai model # https://pythonprogrammingsnippets.tumblr.com import torch device = torch.device("cpu") from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/context-only-question-generator") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/context-only-question-generator").to(device) def get_questions_for_context(context, model, tokenizer, num_count=5): inputs = tokenizer(context, return_tensors="pt") with torch.no_grad(): outputs = model.generate(**inputs, num_beams=num_count, num_return_sequences=num_count) return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs] def get_question_for_context(context, model, tokenizer): return get_questions_for_context(context, model, tokenizer)[0] # send array of sentences, and the function will return an array of questions def context_sentences_to_questions(context, model, tokenizer): questions = [] for sentence in context.split("."): if len(sentence) < 1: continue # skip blanks question = get_question_for_context(sentence, model, tokenizer) questions.append(question) return questions

example 1 (split a string by "." and process):

context = "The capital of France is Paris." context += "The capital of Germany is Berlin." context += "The capital of Spain is Madrid." context += "He is a dog named Robert." if len(context.split(".")) > 2: questions = [] for sentence in context.split("."): if len(sentence) < 1: continue # skip blanks question = get_question_for_context(sentence, model, tokenizer) questions.append(question) print(questions) else: question = get_question_for_context(context, model, tokenizer) print(question)

output:

['What is the capital of France?', 'What is the capital of Germany?', 'What is the capital of Spain?', 'Who is Robert?']

example 2 (generate multiple questions for a given context):

print("\r\n\r\n") context = "She walked to the store to buy a jug of milk." print("Context:\r\n", context) print("") questions = get_questions_for_context(context, model, tokenizer, num_count=15) # pretty print all the questions print("Generated Questions:") for question in questions: print(question) print("\r\n\r\n")

output:

Generated Questions: Where did she go to buy milk? What did she walk to the store to buy? Why did she walk to the store to buy milk? Why did she go to the store? Why did she go to the grocery store? What did she go to the store to buy? Where did the woman go to buy milk? Why did she go to the store to buy milk? What did she buy at the grocery store? Why did she walk to the store? What kind of milk did she buy at the store? Where did she walk to buy milk? What kind of milk did she buy? Where did she go to get milk? What did she buy at the store?

and if we wanted to answer those questions (ez pz):

# now generate an answer for a given question from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/tinyroberta-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/tinyroberta-squad2") def get_answer_for_question(question, context, model, tokenizer): inputs = tokenizer(question, context, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax() predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) target_start_index = torch.tensor([14]) target_end_index = torch.tensor([15]) outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index) loss = outputs.loss answer = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) return answer print("Context:\r\n", context, "\r\n") for question in questions: # right pad the question to 60 characters question_text = question.ljust(50) answer = get_answer_for_question(question, context, model, tokenizer) print("Question: ", question_text, "Answer: ", answer)

[xfebruary]

i dont want natural language generators to be coached into sounding like me and being endlessly apologetic i want them to say weird and meaningless words i want them to show the randomness of statistics and the patterns of language and the emptiness below it i want to see beautiful colors spread across a canvas not a derivative recreation of a mondrian

[multi-fandom-veteran-writer]

🎉🎊Big News!!🎊🎉

So I'm sure most of you have noticed that I haven't been posting anything Vex related in a while. I have an explanation for that. I recently got into Minecraft and I have a pretty big project planned. In case you didn't know, Animation has been my dream since I was younger. Well, I have been experimenting with Blender and the add-on MCprep. Someone told me about a program called Mine-imitator. I know what you are thinking... Since it is so easy, it is not a true animation program. Well, in all honesty, it is perfect for me since I can get the hang of animating, and then whatever I make in Mine-Imator I can then export over to Blender. I loved the animations that you can make using Mine-imator. I have also been in the process of making this huge Minecraft AU while using a role-play text generator. Now just hear me out here. I know that some of you are against that kind of thing but I only used it to help me flesh out my universe and to give me ideas, it also helped my writing skills immensely and helped me to discover my writing style. Please stick with me and bear with me, I promise you that it will be worth your while. I am also going to be making a separate Tumblr for all my Minecraft stuff, including animations and my AU. I look forward to making this and I hope everyone will enjoy my content.

[bastard-of-the-barrel]

So I'm back with games with machine learning! This time I had two tasks: name generation and text generation. The first was funny, but I'll tell more about that another time. As you might guess, I decided to train the text generation model on the books of Leigh Bardugo. I chose "Six of Crows" and "Crooked Kingdom".

The model is trained for a looooong time. A very long time, and I'm terribly impatient. However, I decided to show you what I have at the moment.

The text is generated until the number of words exceeds 60. As you can see from the passages below, the model got confused with punctuation marks at first, but then gradually began to add words. It turned out to be a very exciting experience. Later I want to increase the number of epochs from 21 to 100 and see what happens.

Epoch 1

train loss: 8.31648

Kaz Brekker Jesper , you of to , of . of . . to the , to , . of , . . of to of the , to the to . of . the , . the to the , the , the . . . to . . the . the . of to , the the . of the to . , to the , of the of of to . . , . the of . of the

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Epoch 2

train loss: 6.48834

Kaz Brekker “ . “ , “ “ the the , ’ ’ ’ the ’ the ’ the , , ’ , ” ’ , , the . . “ ’ , the ” , ” , . , . the ’ . . . ’ the the . the . , , ’ , . “ the the ” “ the ’ . “ “ the “ . “ the . the ’ . “ the . ’ the .

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Epoch 8

train loss: 6.01746

Kaz Brekker Jesper . He was . He . “ I . I to a to the ” The , . I . She the , but . The , ” He to the . I ’ s . I ’ t . The ’ . I to , and a . I the . He . She , . “ I “ I . She , but ” “ I “ I . “ the and ” The ’ s ’

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Epoch 9

train loss: 5.93933

Kaz Brekker Jesper “ “ You ’ . “ The , the the , . The and ” “ “ “ “ “ The and and , the and the ” He . He . He was a the , but the and , the , ” The , ” The , . She to his to . The ’ s the and , ” “ You ” the and the the . He . “ “ The ’ s to ,

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Epoch 12

train loss: 5.72819

Kaz Brekker can ’ ll he could his to her . She was ’ d , the , and a . “ The ” He , and a ’ t to her to the ” He said , but , but ” said to a ” “ “ It ’ ll his . The the . She had been . He . I said . He was was to his and the , and his , and ” “ “ It was

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Epoch 13

train loss: 5.66486

Kaz Brekker and the but the Barrel of her to his ” Nina was was ’ re a ” The and and the the but and ” “ “ The he , the other to be . She , the the ” Kaz said to a , the and and ” “ It ’ ll a . “ I said . They , he was was was ’ t to her ” She ’ s a few . “ The and a

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Epoch 16

train loss: 5.49819

Kaz Brekker can can said You ’ re a way . The other . The he had ’ s . “ What he was ’ s , ” Nina , but you said ” “ It said . She said , but the ” “ The ” “ The other , and you said the other . Kaz had the Barrel and his and ” said . She could his , the and he said ” Nina ’ re , but she

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Epoch 17

train loss: 5.44922

Kaz Brekker t know Jesper . It ’ d been . “ The ” “ What ’ re the way ” Kaz ’ re his father ’ d a way , he could a few to the way to his , but the Ice the ” said ” Nina ’ ll her and his and his father , he could you , the ” Kaz said to her and the way . The he had to a little , the Ice ’

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Epoch 19

train loss: 5.35786

Kaz Brekker was face and you have a little , he said , he said . Kaz said . She was had a , ” said , and a way . “ It could have , he could his hands , but he said , he could have been to his hands , ” said the the way to a , but the ” he had the other . She could be , the other and she had a little . She

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Epoch 20

train loss: 5.31483

Kaz Brekker d “ The other ” “ The guards ? I ’ ll be a way of a few . I was to the door to be ” Nina ’ t a few . The boy ’ d ’ re the ” “ The boy , but she was ’ t be a few and his head of . “ It could the city and he was a little ” Wylan was . The other , but you said ” “

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Epoch 21

train loss: 5.27195

Kaz Brekker d said ” “ You can had been , but she had been the ” he said . Kaz . He ’ t be a , and she ’ t the the city and he said . They could a long in the ” “ You can was . I had a hand , he was to a little ” Kaz was the way of his eyes of . He had ’ re her and the the other of his

...Then I tried to train the model on the Russian version of the books, and the result was much better already in the first epochs. At the moment, the model is still being trained, and texts looks a little stupid, but here are results anyway:

Epoch 2

train loss: 5.78698

Каз Бреккер . Он не был покрыт и он услышал его и , но он не мог понять в этом . – Это не было времени ? Он не хочу . Матиас почувствовал его за дверью на нее с Матиасом и в конце , и они все равно знал с ним – он с тобой , что ты хочешь . Когда она не было . – Что я не могу его ? Джеспер кивнул – и на них и не будет ждать

Epoch 3

train loss: 5.12968

Каз Бреккер , но не не не мог понять . – Это был готов , что я хочу сказать на него , чтобы я бы тебе , когда ты хочешь . Джеспер знал об этом , и все это напомнило Инеж с Матиасом в том месте . Он думал ? – Нет не не было назвать . Он знал ли они в том с пульсом ? Он взял бровь . Каз был не было бы быть . – Что , что ты

Epoch 4

train loss: 4.61884

Каз Бреккер услышал приближающийся топот глиной . « Я бы хотел , и все его лицо – Призрак . Он был весь количество , но и так не остановятся в том направлении и не так далеко . Джеспер присел рядом с Матиасом . Матиас почувствовал легкую на него , но все его пристрелить ее в воздухе с пульсом на песке . « Я бы предпочел забрать раз , когда я не мог перестать лифт , что я не могу заставить ее жизни

[spankymerve]

What I've Been Reading: December 4, 2022

What I’ve Been Reading: December 4, 2022

I’m baaaaack! Let’s chat about Twitter, machine learning, and universities. (more…)

[ai-azura]

Exploring the Benefits and Risks of ChatGPT in Education: How Responsible Use Can Transform the Classroom Experience

Exploring the Benefits and Risks of ChatGPT in Education: How Responsible Use Can Transform the Classroom Experience

ChatGPT is a powerful language processing tool that has the potential to revolutionize the way we interact with computers and access information. However, it is important to consider the potential risks and ethical implications of this technology. In education, chatGPT and similar tools can have a number of benefits. For example, they could help shift the focus of education from the rehearsal of…

[thedevmaster-tdm]

STOP Using Fake Human Faces in AI

[redysetdare]

Sometimes...characters being in a romantic relationship is worse.

[mynnthia]

was talking with a friend about how some of dunmeshi fаndom misunderstands kabru's initial feelings towards laios.

to sum up kabru's situation via a self-contained modernized metaphor:

kabru is like a guy who lost his entire family in a highly traumatic car accident. years later he joins a discord server and takes note of laios, another server member who seems interesting, so they start chatting. then laios reveals his special interest and favorite movie of all time is David Cronenberg's Crash (1996), and invites kabru to go watch a demolition derby with him

[kipkiphoorayy]

‼️ hazbin s1 finale spoilers ‼️

some text posts for the finale, what a ride!

[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.