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| import gradio as gr | |
| import math | |
| import spacy | |
| from datasets import load_dataset | |
| from sentence_transformers import SentenceTransformer | |
| from sentence_transformers import InputExample | |
| from sentence_transformers import losses | |
| from sentence_transformers import util | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification | |
| from transformers import TrainingArguments, Trainer | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.utils.data import DataLoader | |
| import numpy as np | |
| import evaluate | |
| import nltk | |
| from nltk.corpus import stopwords | |
| import subprocess | |
| import sys | |
| import random | |
| # !pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl | |
| subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl']) | |
| # tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') | |
| model_base = "bert-analogies" | |
| nltk.download('stopwords') | |
| nlp = spacy.load("en_core_web_sm") | |
| stops = stopwords.words("english") | |
| ROMAN_CONSTANTS = ( | |
| ( "", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX" ), | |
| ( "", "X", "XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC" ), | |
| ( "", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM" ), | |
| ( "", "M", "MM", "MMM", "", "", "-", "", "", "" ), | |
| ( "", "i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix" ), | |
| ( "", "x", "xx", "xxx", "xl", "l", "lx", "lxx", "lxxx", "xc" ), | |
| ( "", "c", "cc", "ccc", "cd", "d", "dc", "dcc", "dccc", "cm" ), | |
| ( "", "m", "mm", "mmm", "", "", "-", "", "", "" ), | |
| ) | |
| # answer = "Pizza" | |
| guesses = [] | |
| return_guesses = [] | |
| answer = "Moon" | |
| word1 = "Black" | |
| word2 = "White" | |
| word3 = "Sun" | |
| base_prompts = ["Sun is to Moon as ", "Black is to White as ", "Atom is to Element as", | |
| "Athens is to Greece as ", "Cat is to Dog as ", "Robin is to Bird as", | |
| "Hunger is to Ambition as "] | |
| #Mean Pooling - Take attention mask into account for correct averaging | |
| def mean_pooling(model_output, attention_mask): | |
| token_embeddings = model_output['token_embeddings'] #First element of model_output contains all token embeddings | |
| input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() | |
| return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) | |
| def normalize(comment, lowercase, remove_stopwords): | |
| if lowercase: | |
| comment = comment.lower() | |
| comment = nlp(comment) | |
| lemmatized = list() | |
| for word in comment: | |
| lemma = word.lemma_.strip() | |
| if lemma: | |
| if not remove_stopwords or (remove_stopwords and lemma not in stops): | |
| lemmatized.append(lemma) | |
| return " ".join(lemmatized) | |
| # def tokenize_function(examples): | |
| # return tokenizer(examples["text"]) | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| predictions = np.argmax(logits, axis=-1) | |
| metric = evaluate.load("accuracy") | |
| return metric.compute(predictions=predictions, references=labels) | |
| def get_model(): | |
| global model_base | |
| model = SentenceTransformer(model_base) | |
| gpu_available = torch.cuda.is_available() | |
| device = torch.device("cuda" if gpu_available else "cpu") | |
| model = model.to(device) | |
| return model | |
| def cosine_scores(model, sentence): | |
| global word1 | |
| global word2 | |
| global word3 | |
| # sentence1 = f"{word1} is to {word2} as" | |
| embeddings1 = model.encode(sentence, convert_to_tensor=True) | |
| def embeddings(model, sentences): | |
| gpu_available = torch.cuda.is_available() | |
| device = torch.device("cuda" if gpu_available else "cpu") | |
| # device = torch.device('cuda:0') | |
| embeddings = model.encode(sentences) | |
| global word1 | |
| global word2 | |
| global word3 | |
| global model_base | |
| # Load model from HuggingFace Hub | |
| tokenizer = AutoTokenizer.from_pretrained(model_base) | |
| encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') | |
| # token_ids = tokenizer.encode(sentences, return_tensors='pt') | |
| # blank_id = tokenizer.mask_token_id | |
| # blank_id_idx = torch.where(encoded_input["input_ids"] == blank_id)[1] | |
| encoded_input["input_ids"] = encoded_input["input_ids"].to(device) | |
| encoded_input["attention_mask"] = encoded_input["attention_mask"].to(device) | |
| encoded_input['token_type_ids'] = encoded_input['token_type_ids'].to(device) | |
| encoded_input['input'] = {'input_ids':encoded_input['input_ids'], 'attention_mask':encoded_input['attention_mask']} | |
| del encoded_input['input_ids'] | |
| del encoded_input['token_type_ids'] | |
| del encoded_input['attention_mask'] | |
| with torch.no_grad(): | |
| # output = model(encoded_input) | |
| print(encoded_input) | |
| model_output = model(**encoded_input) | |
| # output = model(encoded_input_topk) | |
| unmasker = pipeline('fill-mask', model=model_base) | |
| guesses = unmasker(sentences) | |
| print(guesses) | |
| # Perform pooling | |
| sentence_embeddings = mean_pooling(model_output, encoded_input['input']["attention_mask"]) | |
| # Normalize embeddings | |
| sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) | |
| potential_words = [] | |
| for guess in guesses: | |
| temp_word = guess['token_str'] | |
| if temp_word[0].isalpha() and temp_word not in stops and temp_word not in ROMAN_CONSTANTS: | |
| potential_words.append(guess['token_str']) | |
| rand_index = random.randint(0, len(potential_words) - 1) | |
| print("THE LENGTH OF POTENTIAL WORDS FOR", sentences, "IS", len(potential_words), "AND THE RANDOM INDEX CHOSEN IS", rand_index) | |
| chosen_word = potential_words[rand_index] | |
| return chosen_word | |
| def random_word(): | |
| global model_base | |
| with open(model_base + '/vocab.txt', 'r') as file: | |
| line = "" | |
| content = file.readlines() | |
| length = len(content) | |
| while line == "": | |
| rand_line = random.randrange(0, length) | |
| if content[rand_line][0].isalpha() and content[rand_line][:-1] not in stops and content[rand_line][:-1] not in ROMAN_CONSTANTS: | |
| line = content[rand_line] | |
| else: | |
| print(f"{content[rand_line]} is not alpha or is a stop word") | |
| # for num, aline in enumerate(file, 1997): | |
| # if random.randrange(num) and aline.isalpha(): | |
| # continue | |
| # # elif not aline.isalpha(): | |
| # line = aline | |
| print(line) | |
| return line[:-1] | |
| def generate_prompt(model): | |
| global word1 | |
| global word2 | |
| global word3 | |
| global answer | |
| global base_prompts | |
| word1 = random_word() | |
| # word2 = random_word() | |
| random_line = random.randint(0, len(base_prompts) - 1) | |
| word2 = embeddings(model, f"{base_prompts[random_line]}{word1} is to [MASK].") | |
| word3 = random_word() | |
| sentence = f"{word1} is to {word2} as {word3} is to [MASK]." | |
| print(sentence) | |
| answer = embeddings(model, sentence) | |
| print("ANSWER IS", answer) | |
| return f"# {word1} is to {word2} as {word3} is to ___." | |
| # cosine_scores(model, sentence) | |
| def new_prompt(name): | |
| model = get_model() | |
| return generate_prompt(model) | |
| def check_answer(guess:str): | |
| global guesses | |
| global answer | |
| global return_guesses | |
| global word1 | |
| global word2 | |
| global word3 | |
| model = get_model() | |
| output = "" | |
| protected_guess = guess | |
| sentence = f"{word1} is to {word2} as [MASK] is to {guess}." | |
| other_word = embeddings(model, sentence) | |
| guesses.append(guess) | |
| for guess in return_guesses: | |
| output += (guess) | |
| # output = output[:-1] | |
| prompt = f"{word1} is to {word2} as {word3} is to ___." | |
| # print("IS", protected_guess, "EQUAL TO", answer, ":", protected_guess.lower() == answer.lower()) | |
| if protected_guess.lower() == answer.lower(): | |
| return_guesses.append(f"<span style='color:green'>- {protected_guess}: {word1} is to {word2} as {word3} is to {protected_guess}.</span><br>") | |
| output += f"<span style='color:green'>- {return_guesses[-1]}</span><br>" | |
| new_prompt = generate_prompt(model) | |
| return new_prompt, "Correct!", output | |
| else: | |
| return_guess = f"- {protected_guess}: {word1} is to {word2} as {other_word} is to {protected_guess}.<br>" | |
| return_guesses.append(return_guess) | |
| output += (return_guess) | |
| return prompt, "Try again!", output | |
| def main(): | |
| global word1 | |
| global word2 | |
| global word3 | |
| global answer | |
| # answer = "Moon" | |
| global guesses | |
| # num_rows, data_type, value, example, embeddings = training() | |
| # sent_embeddings = embeddings() | |
| model = get_model() | |
| generate_prompt(model) | |
| prompt = f"# {word1} is to {word2} as {word3} is to ____" | |
| print(prompt) | |
| print("TESTING EMBEDDINGS") | |
| with gr.Blocks() as iface: | |
| mark_question = gr.Markdown(prompt) | |
| with gr.Tab("Guess"): | |
| text_input = gr.Textbox() | |
| text_output = gr.Textbox() | |
| text_button = gr.Button("Submit") | |
| prompt_button = gr.Button("New Prompt") | |
| with gr.Accordion("Open for previous guesses"): | |
| text_guesses = gr.Markdown() | |
| # with gr.Tab("Testing"): | |
| # gr.Markdown(f"""The Embeddings are {sent_embeddings}.""") | |
| text_button.click(check_answer, inputs=[text_input], outputs=[mark_question, text_output, text_guesses]) | |
| promt_button.click(new_prompt, inputs=[], outputs=[mark_question]) | |
| # iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| iface.launch() | |
| if __name__ == "__main__": | |
| main() |