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3139db4
1
Parent(s):
33e257e
This time for sure x4
Browse files- app_context.py +253 -257
- flan-t5-train.py +234 -301
- results/checkpoint-16000/added_tokens.json +102 -0
- results/checkpoint-16000/config.json +62 -0
- results/checkpoint-16000/generation_config.json +6 -0
- results/checkpoint-16000/model.safetensors +3 -0
- results/checkpoint-16000/optimizer.pt +3 -0
- results/checkpoint-16000/rng_state.pth +3 -0
- results/checkpoint-16000/scheduler.pt +3 -0
- results/checkpoint-16000/special_tokens_map.json +125 -0
- results/checkpoint-16000/spiece.model +3 -0
- results/checkpoint-16000/tokenizer_config.json +939 -0
- results/checkpoint-16000/trainer_state.json +319 -0
- results/checkpoint-16000/training_args.bin +3 -0
- results/checkpoint-16500/added_tokens.json +102 -0
- results/checkpoint-16500/config.json +62 -0
- results/checkpoint-16500/generation_config.json +6 -0
- results/checkpoint-16500/model.safetensors +3 -0
- results/checkpoint-16500/optimizer.pt +3 -0
- results/checkpoint-16500/rng_state.pth +3 -0
- results/checkpoint-16500/scheduler.pt +3 -0
- results/checkpoint-16500/special_tokens_map.json +125 -0
- results/checkpoint-16500/spiece.model +3 -0
- results/checkpoint-16500/tokenizer_config.json +939 -0
- results/checkpoint-16500/trainer_state.json +325 -0
- results/checkpoint-16500/training_args.bin +3 -0
- results/checkpoint-17000/added_tokens.json +102 -0
- results/checkpoint-17000/config.json +62 -0
- results/checkpoint-17000/generation_config.json +6 -0
- results/checkpoint-17000/model.safetensors +3 -0
- results/checkpoint-17000/optimizer.pt +3 -0
- results/checkpoint-17000/rng_state.pth +3 -0
- results/checkpoint-17000/scheduler.pt +3 -0
- results/checkpoint-17000/special_tokens_map.json +125 -0
- results/checkpoint-17000/spiece.model +3 -0
- results/checkpoint-17000/tokenizer_config.json +939 -0
- results/checkpoint-17000/trainer_state.json +331 -0
- results/checkpoint-17000/training_args.bin +3 -0
- word_embedding.py +619 -0
app_context.py
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import gradio as gr
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import math
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import spacy
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from datasets import load_dataset
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from
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print(inputs)
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global
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#
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with gr.
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if __name__ == "__main__":
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main()
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import gradio as gr
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import math
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import spacy
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from datasets import load_dataset
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from transformers import pipeline, T5Tokenizer
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from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
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from transformers import TrainingArguments, Trainer, T5ForConditionalGeneration
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import torch
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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import numpy as np
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import evaluate
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import nltk
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from nltk.corpus import stopwords
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import subprocess
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import sys
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import random
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from textwrap import fill
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# !pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl
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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'])
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# tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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model_base = "results/checkpoint-17000"
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nltk.download('stopwords')
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nlp = spacy.load("en_core_web_sm")
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stops = stopwords.words("english")
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ROMAN_CONSTANTS = (
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( "", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX" ),
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( "", "X", "XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC" ),
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( "", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM" ),
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( "", "M", "MM", "MMM", "", "", "-", "", "", "" ),
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( "", "i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix" ),
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( "", "x", "xx", "xxx", "xl", "l", "lx", "lxx", "lxxx", "xc" ),
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( "", "c", "cc", "ccc", "cd", "d", "dc", "dcc", "dccc", "cm" ),
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( "", "m", "mm", "mmm", "", "", "-", "", "", "" ),
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)
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# answer = "Pizza"
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guesses = []
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return_guesses = []
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answer = "Moon"
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word1 = "Black"
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word2 = "White"
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word3 = "Sun"
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base_prompts = ["Sun is to Moon as ", "Black is to White as ", "Atom is to Element as",
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"Athens is to Greece as ", "Cat is to Dog as ", "Robin is to Bird as",
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"Hunger is to Ambition as "]
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output['token_embeddings'] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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def normalize(comment, lowercase, remove_stopwords):
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if lowercase:
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comment = comment.lower()
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comment = nlp(comment)
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lemmatized = list()
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for word in comment:
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lemma = word.lemma_.strip()
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if lemma:
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if not remove_stopwords or (remove_stopwords and lemma not in stops):
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lemmatized.append(lemma)
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return " ".join(lemmatized)
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# def tokenize_function(examples):
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# return tokenizer(examples["text"])
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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predictions = np.argmax(logits, axis=-1)
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metric = evaluate.load("accuracy")
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return metric.compute(predictions=predictions, references=labels)
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def get_model():
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global model_base
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# last_checkpoint = "./results/checkpoint-22500"
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finetuned_model = T5ForConditionalGeneration.from_pretrained(model_base)
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tokenizer = T5Tokenizer.from_pretrained(model_base)
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# model = SentenceTransformer(model_base)
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gpu_available = torch.cuda.is_available()
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device = torch.device("cuda" if gpu_available else "cpu")
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finetuned_model = finetuned_model.to(device)
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return finetuned_model, tokenizer
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def cosine_scores(model, sentence):
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global word1
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global word2
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global word3
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# sentence1 = f"{word1} is to {word2} as"
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embeddings1 = model.encode(sentence, convert_to_tensor=True)
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def embeddings(model, sentences, tokenizer):
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global word1
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global word2
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global word3
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global model_base
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gpu_available = torch.cuda.is_available()
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device = torch.device("cuda" if gpu_available else "cpu")
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# device = torch.device('cuda:0')
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# embeddings = model.encode(sentences)
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question = "Please answer to this question: " + sentences
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inputs = tokenizer(question, return_tensors="pt")
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print(inputs)
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# print(inputs.device)
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print(model.device)
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print(inputs['input_ids'].device)
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print(inputs['attention_mask'].device)
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inputs['attention_mask'] = inputs['attention_mask'].to(device)
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inputs['input_ids'] = inputs['input_ids'].to(device)
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outputs = model.generate(**inputs)
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answer = tokenizer.decode(outputs[0])
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answer = answer[6:-4]
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# print(fill(answer, width=80))
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print("ANSWER IS", answer)
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return answer
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def random_word(model, tokenizer):
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global model_base
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vocab = tokenizer.get_vocab()
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# with open(model_base + '/vocab.txt', 'r') as file:
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line = ""
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# content = file.readlines()
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length = tokenizer.vocab_size
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# print(vocab)
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while line == "":
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rand_line = random.randrange(0, length)
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# print("TRYING TO FIND", rand_line, "OUT OF", length, "WITH VOCAB OF TYPE", type(vocab))
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for word, id in vocab.items():
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if id == rand_line and word[0].isalpha() and word not in stops and word not in ROMAN_CONSTANTS:
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# if vocab[rand_line][0].isalpha() and vocab[rand_line][:-1] not in stops and vocab[rand_line][:-1] not in ROMAN_CONSTANTS:
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line = word
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elif id == rand_line:
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print(f"{word} is not alpha or is a stop word")
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# for num, aline in enumerate(file, 1997):
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# if random.randrange(num) and aline.isalpha():
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# continue
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# # elif not aline.isalpha():
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# line = aline
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print(line)
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return line
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def generate_prompt(model, tokenizer):
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global word1
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global word2
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global word3
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global answer
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global base_prompts
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word1 = random_word(model, tokenizer)
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# word2 = random_word()
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word2 = embeddings(model, f"{base_prompts[random.randint(0, len(base_prompts) - 1)]}{word1} is to ___.", tokenizer)
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word3 = random_word(model, tokenizer)
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sentence = f"{word1} is to {word2} as {word3} is to ___."
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print(sentence)
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answer = embeddings(model, sentence, tokenizer)
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print("ANSWER IS", answer)
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return f"# {word1} is to {word2} as {word3} is to ___."
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# cosine_scores(model, sentence)
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def greet(name):
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return "Hello " + name + "!!"
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def check_answer(guess:str):
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global guesses
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global answer
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global return_guesses
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global word1
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global word2
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global word3
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model, tokenizer = get_model()
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output = ""
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protected_guess = guess
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sentence = f"{word1} is to {word2} as [MASK] is to {guess}."
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other_word = embeddings(model, sentence, tokenizer)
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guesses.append(guess)
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for guess in return_guesses:
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output += ("- " + guess + "<br>")
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# output = output[:-1]
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prompt = f"{word1} is to {word2} as {word3} is to ___."
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# print("IS", protected_guess, "EQUAL TO", answer, ":", protected_guess.lower() == answer.lower())
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if protected_guess.lower() == answer.lower():
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return_guesses.append(f"{protected_guess}: {word1} is to {word2} as {word3} is to {protected_guess}.")
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output += f"<span style='color:green'>- {return_guesses[-1]}</span><br>"
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new_prompt = generate_prompt(model, tokenizer)
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return new_prompt, "Correct!", output
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else:
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+
return_guess = f"{protected_guess}: {word1} is to {word2} as {other_word} is to {protected_guess}."
|
214 |
+
return_guesses.append(return_guess)
|
215 |
+
output += ("- " + return_guess + " <br>")
|
216 |
+
return prompt, "Try again!", output
|
217 |
+
|
218 |
+
def main():
|
219 |
+
global word1
|
220 |
+
global word2
|
221 |
+
global word3
|
222 |
+
global answer
|
223 |
+
# answer = "Moon"
|
224 |
+
global guesses
|
225 |
+
|
226 |
+
|
227 |
+
# num_rows, data_type, value, example, embeddings = training()
|
228 |
+
# sent_embeddings = embeddings()
|
229 |
+
model, tokenizer = get_model()
|
230 |
+
generate_prompt(model, tokenizer)
|
231 |
+
|
232 |
+
prompt = f"{word1} is to {word2} as {word3} is to ____"
|
233 |
+
print(prompt)
|
234 |
+
print("TESTING EMBEDDINGS")
|
235 |
+
with gr.Blocks() as iface:
|
236 |
+
mark_question = gr.Markdown(prompt)
|
237 |
+
with gr.Tab("Guess"):
|
238 |
+
text_input = gr.Textbox()
|
239 |
+
text_output = gr.Textbox()
|
240 |
+
text_button = gr.Button("Submit")
|
241 |
+
with gr.Accordion("Open for previous guesses"):
|
242 |
+
text_guesses = gr.Markdown()
|
243 |
+
# with gr.Tab("Testing"):
|
244 |
+
# gr.Markdown(f"""The Embeddings are {sent_embeddings}.""")
|
245 |
+
text_button.click(check_answer, inputs=[text_input], outputs=[mark_question, text_output, text_guesses])
|
246 |
+
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
247 |
+
iface.launch()
|
248 |
+
|
249 |
+
|
250 |
+
|
251 |
+
|
252 |
+
|
253 |
+
if __name__ == "__main__":
|
|
|
|
|
|
|
|
|
254 |
main()
|
flan-t5-train.py
CHANGED
@@ -1,302 +1,235 @@
|
|
1 |
-
import gradio as gr
|
2 |
-
import math
|
3 |
-
from datasets import load_dataset
|
4 |
-
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
|
5 |
-
from transformers import TrainingArguments, Trainer
|
6 |
-
from transformers import T5Tokenizer, T5ForConditionalGeneration
|
7 |
-
import torch
|
8 |
-
import torch.nn.functional as F
|
9 |
-
from torch.utils.data import DataLoader
|
10 |
-
import numpy as np
|
11 |
-
import evaluate
|
12 |
-
import nltk
|
13 |
-
from nltk.corpus import stopwords
|
14 |
-
import subprocess
|
15 |
-
import sys
|
16 |
-
from transformers import T5Tokenizer, DataCollatorForSeq2Seq
|
17 |
-
from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
|
18 |
-
from transformers import DataCollatorWithPadding, DistilBertTokenizerFast
|
19 |
-
from transformers import TrainingArguments
|
20 |
-
from transformers import (
|
21 |
-
BertModel,
|
22 |
-
BertTokenizerFast,
|
23 |
-
Trainer,
|
24 |
-
EvalPrediction
|
25 |
-
)
|
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-
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-
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-
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-
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#
|
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|
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-
)
|
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|
60 |
-
|
61 |
-
#
|
62 |
-
|
63 |
-
|
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-
|
65 |
-
return
|
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|
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-
|
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-
#
|
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-
|
70 |
-
|
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-
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-
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-
|
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-
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-
#
|
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-
|
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-
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-
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|
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-
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-
|
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-
|
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-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
print("
|
103 |
-
|
104 |
-
|
105 |
-
#
|
106 |
-
|
107 |
-
|
108 |
-
|
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-
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-
|
111 |
-
|
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-
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-
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-
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|
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-
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|
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-
|
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-
|
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-
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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-
|
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|
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-
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-
|
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-
|
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|
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|
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-
|
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-
|
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-
|
187 |
-
|
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-
|
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-
|
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-
|
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-
|
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-
#
|
193 |
-
|
194 |
-
|
195 |
-
|
196 |
-
|
197 |
-
|
198 |
-
#
|
199 |
-
|
200 |
-
|
201 |
-
|
202 |
-
|
203 |
-
|
204 |
-
|
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-
|
206 |
-
|
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-
|
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-
|
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-
|
210 |
-
|
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-
|
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-
|
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-
|
214 |
-
|
215 |
-
|
216 |
-
|
217 |
-
|
218 |
-
|
219 |
-
|
220 |
-
|
221 |
-
|
222 |
-
|
223 |
-
|
224 |
-
|
225 |
-
|
226 |
-
|
227 |
-
|
228 |
-
|
229 |
-
|
230 |
-
|
231 |
-
|
232 |
-
|
233 |
-
|
234 |
-
|
235 |
-
eval_dataset=dataset["test"],
|
236 |
-
# evaluation_strategy="no"
|
237 |
-
tokenizer=tokenizer,
|
238 |
-
data_collator=data_collator,
|
239 |
-
compute_metrics=compute_metrics
|
240 |
-
)
|
241 |
-
|
242 |
-
# model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10)
|
243 |
-
|
244 |
-
trainer.train()
|
245 |
-
|
246 |
-
# model.save("flan-analogies")
|
247 |
-
|
248 |
-
# model.save_to_hub("smhavens/bert-base-analogies")
|
249 |
-
# accuracy = compute_metrics(eval, metric)
|
250 |
-
return 0
|
251 |
-
|
252 |
-
def greet(name):
|
253 |
-
return "Hello " + name + "!!"
|
254 |
-
|
255 |
-
def check_answer(guess:str):
|
256 |
-
global guesses
|
257 |
-
global answer
|
258 |
-
guesses.append(guess)
|
259 |
-
output = ""
|
260 |
-
for guess in guesses:
|
261 |
-
output += ("- " + guess + "\n")
|
262 |
-
output = output[:-1]
|
263 |
-
|
264 |
-
if guess.lower() == answer.lower():
|
265 |
-
return "Correct!", output
|
266 |
-
else:
|
267 |
-
return "Try again!", output
|
268 |
-
|
269 |
-
def main():
|
270 |
-
print("BEGIN")
|
271 |
-
word1 = "Black"
|
272 |
-
word2 = "White"
|
273 |
-
word3 = "Sun"
|
274 |
-
global answer
|
275 |
-
answer = "Moon"
|
276 |
-
global guesses
|
277 |
-
|
278 |
-
training()
|
279 |
-
|
280 |
-
# prompt = f"{word1} is to {word2} as {word3} is to ____"
|
281 |
-
# with gr.Blocks() as iface:
|
282 |
-
# gr.Markdown(prompt)
|
283 |
-
# with gr.Tab("Guess"):
|
284 |
-
# text_input = gr.Textbox()
|
285 |
-
# text_output = gr.Textbox()
|
286 |
-
# text_button = gr.Button("Submit")
|
287 |
-
# with gr.Accordion("Open for previous guesses"):
|
288 |
-
# text_guesses = gr.Textbox()
|
289 |
-
# with gr.Tab("Testing"):
|
290 |
-
# gr.Markdown(f"""Number of rows in dataset is {num_rows}, with each having type {data_type} and value {value}.
|
291 |
-
# An example is {example}.
|
292 |
-
# The Embeddings are {embeddings}.""")
|
293 |
-
# text_button.click(check_answer, inputs=[text_input], outputs=[text_output, text_guesses])
|
294 |
-
# # iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
295 |
-
# iface.launch()
|
296 |
-
|
297 |
-
|
298 |
-
|
299 |
-
|
300 |
-
|
301 |
-
if __name__ == "__main__":
|
302 |
main()
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import math
|
3 |
+
from datasets import load_dataset
|
4 |
+
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
|
5 |
+
from transformers import TrainingArguments, Trainer
|
6 |
+
from transformers import T5Tokenizer, T5ForConditionalGeneration
|
7 |
+
import torch
|
8 |
+
import torch.nn.functional as F
|
9 |
+
from torch.utils.data import DataLoader
|
10 |
+
import numpy as np
|
11 |
+
import evaluate
|
12 |
+
import nltk
|
13 |
+
from nltk.corpus import stopwords
|
14 |
+
import subprocess
|
15 |
+
import sys
|
16 |
+
from transformers import T5Tokenizer, DataCollatorForSeq2Seq
|
17 |
+
from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
|
18 |
+
from transformers import DataCollatorWithPadding, DistilBertTokenizerFast
|
19 |
+
from transformers import TrainingArguments
|
20 |
+
from transformers import (
|
21 |
+
BertModel,
|
22 |
+
BertTokenizerFast,
|
23 |
+
Trainer,
|
24 |
+
EvalPrediction
|
25 |
+
)
|
26 |
+
|
27 |
+
nltk.download("punkt", quiet=True)
|
28 |
+
metric = evaluate.load("rouge")
|
29 |
+
|
30 |
+
# Global Parameters
|
31 |
+
L_RATE = 3e-4
|
32 |
+
BATCH_SIZE = 8
|
33 |
+
PER_DEVICE_EVAL_BATCH = 4
|
34 |
+
WEIGHT_DECAY = 0.01
|
35 |
+
SAVE_TOTAL_LIM = 3
|
36 |
+
NUM_EPOCHS = 10
|
37 |
+
|
38 |
+
# Set up training arguments
|
39 |
+
training_args = Seq2SeqTrainingArguments(
|
40 |
+
output_dir="./results",
|
41 |
+
evaluation_strategy="epoch",
|
42 |
+
learning_rate=L_RATE,
|
43 |
+
per_device_train_batch_size=BATCH_SIZE,
|
44 |
+
per_device_eval_batch_size=PER_DEVICE_EVAL_BATCH,
|
45 |
+
weight_decay=WEIGHT_DECAY,
|
46 |
+
save_total_limit=SAVE_TOTAL_LIM,
|
47 |
+
num_train_epochs=NUM_EPOCHS,
|
48 |
+
predict_with_generate=True,
|
49 |
+
push_to_hub=False
|
50 |
+
)
|
51 |
+
|
52 |
+
model_id = "google/flan-t5-base"
|
53 |
+
tokenizer = T5Tokenizer.from_pretrained(model_id)
|
54 |
+
# tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
|
55 |
+
# metric = evaluate.load("accuracy")
|
56 |
+
|
57 |
+
def tokenize_function(examples):
|
58 |
+
return tokenizer(examples["stem"], padding="max_length", truncation=True)
|
59 |
+
|
60 |
+
|
61 |
+
#Mean Pooling - Take attention mask into account for correct averaging
|
62 |
+
def mean_pooling(model_output, attention_mask):
|
63 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
64 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
65 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
66 |
+
|
67 |
+
|
68 |
+
# def compute_metrics(eval_pred):
|
69 |
+
# logits, labels = eval_pred
|
70 |
+
# predictions = np.argmax(logits, axis=-1)
|
71 |
+
# metric = evaluate.load("accuracy")
|
72 |
+
# return metric.compute(predictions=predictions, references=labels)
|
73 |
+
|
74 |
+
def compute_metrics(eval_preds):
|
75 |
+
preds, labels = eval_preds
|
76 |
+
|
77 |
+
# decode preds and labels
|
78 |
+
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
|
79 |
+
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
80 |
+
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
81 |
+
|
82 |
+
# rougeLSum expects newline after each sentence
|
83 |
+
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
|
84 |
+
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
|
85 |
+
|
86 |
+
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
87 |
+
|
88 |
+
return result
|
89 |
+
|
90 |
+
|
91 |
+
def training():
|
92 |
+
dataset_id = "tomasmcz/word2vec_analogy"
|
93 |
+
# dataset_id = "relbert/scientific_and_creative_analogy"
|
94 |
+
# dataset_sub = "Quadruples_Kmiecik_random_split"
|
95 |
+
print("GETTING DATASET")
|
96 |
+
dataset = load_dataset(dataset_id)
|
97 |
+
# dataset = dataset["train"]
|
98 |
+
# tokenized_datasets = dataset.map(tokenize_function, batched=True)
|
99 |
+
|
100 |
+
print(dataset)
|
101 |
+
print(f"- The {dataset_id} dataset has {dataset['train'].num_rows} examples.")
|
102 |
+
print(f"- Each example is a {type(dataset['train'][0])} with a {type(dataset['train'][0])} as value.")
|
103 |
+
print(f"- Examples look like this: {dataset['train'][0]}")
|
104 |
+
|
105 |
+
# for i in dataset["train"]:
|
106 |
+
# print(i["AB"], "to", i["CD"], "is", i["label"])
|
107 |
+
|
108 |
+
dataset = dataset["train"].train_test_split(test_size=0.3)
|
109 |
+
|
110 |
+
# We prefix our tasks with "answer the question"
|
111 |
+
prefix = "Please answer this question: "
|
112 |
+
|
113 |
+
|
114 |
+
def preprocess_function(examples):
|
115 |
+
"""Add prefix to the sentences, tokenize the text, and set the labels"""
|
116 |
+
# The "inputs" are the tokenized answer:
|
117 |
+
inputs = []
|
118 |
+
# print(examples)
|
119 |
+
# inputs = [prefix + doc for doc in examples["question"]]
|
120 |
+
for doc in examples['word_a']:
|
121 |
+
# print("THE DOC IS:", doc)
|
122 |
+
# print("THE DOC IS:", examples[i]['AB'], examples[i]['CD'], examples[i]['label'])
|
123 |
+
prompt = f"{prefix}{doc} is to "
|
124 |
+
inputs.append(prompt)
|
125 |
+
# inputs = [prefix + doc for doc in examples["question"]]
|
126 |
+
for indx, doc in enumerate(examples["word_b"]):
|
127 |
+
prompt = f"{doc} as "
|
128 |
+
inputs[indx] += prompt
|
129 |
+
|
130 |
+
for indx, doc in enumerate(examples["word_c"]):
|
131 |
+
prompt = f"{doc} is to ___."
|
132 |
+
inputs[indx] += prompt
|
133 |
+
model_inputs = tokenizer(inputs, max_length=128, truncation=True)
|
134 |
+
|
135 |
+
# print(examples["label"], type(examples["label"]))
|
136 |
+
|
137 |
+
# The "labels" are the tokenized outputs:
|
138 |
+
labels = tokenizer(text_target=examples["word_d"],
|
139 |
+
max_length=512,
|
140 |
+
truncation=True)
|
141 |
+
|
142 |
+
model_inputs["labels"] = labels["input_ids"]
|
143 |
+
return model_inputs
|
144 |
+
|
145 |
+
|
146 |
+
|
147 |
+
# Map the preprocessing function across our dataset
|
148 |
+
tokenized_dataset = dataset.map(preprocess_function, batched=True)
|
149 |
+
|
150 |
+
print("END DATALOADER")
|
151 |
+
|
152 |
+
# print(train_examples)
|
153 |
+
|
154 |
+
embeddings = finetune(tokenized_dataset)
|
155 |
+
|
156 |
+
return 0
|
157 |
+
|
158 |
+
|
159 |
+
def finetune(dataset):
|
160 |
+
# model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
161 |
+
# model_id = "sentence-transformers/all-MiniLM-L6-v2"
|
162 |
+
model_id = "google/flan-t5-base"
|
163 |
+
# model_id = "distilbert-base-uncased"
|
164 |
+
# tokenizer = DistilBertTokenizerFast.from_pretrained(model_id)
|
165 |
+
tokenizer = T5Tokenizer.from_pretrained(model_id)
|
166 |
+
model = T5ForConditionalGeneration.from_pretrained(model_id)
|
167 |
+
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
|
168 |
+
device = torch.device('cuda:0')
|
169 |
+
model = model.to(device)
|
170 |
+
|
171 |
+
# training_args = TrainingArguments(output_dir="test_trainer")
|
172 |
+
|
173 |
+
# USE THIS LINK
|
174 |
+
# https://huggingface.co/blog/how-to-train-sentence-transformers
|
175 |
+
|
176 |
+
# train_loss = losses.MegaBatchMarginLoss(model=model)
|
177 |
+
# ds_train, ds_valid = dataset.train_test_split(test_size=0.2, seed=42)
|
178 |
+
|
179 |
+
print("BEGIN FIT")
|
180 |
+
|
181 |
+
trainer = Seq2SeqTrainer(
|
182 |
+
model=model,
|
183 |
+
args=training_args,
|
184 |
+
train_dataset=dataset["train"],
|
185 |
+
eval_dataset=dataset["test"],
|
186 |
+
# evaluation_strategy="no"
|
187 |
+
tokenizer=tokenizer,
|
188 |
+
data_collator=data_collator,
|
189 |
+
compute_metrics=compute_metrics
|
190 |
+
)
|
191 |
+
|
192 |
+
# model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10)
|
193 |
+
|
194 |
+
trainer.train()
|
195 |
+
|
196 |
+
# model.save("flan-analogies")
|
197 |
+
|
198 |
+
# model.save_to_hub("smhavens/bert-base-analogies")
|
199 |
+
# accuracy = compute_metrics(eval, metric)
|
200 |
+
return 0
|
201 |
+
|
202 |
+
def greet(name):
|
203 |
+
return "Hello " + name + "!!"
|
204 |
+
|
205 |
+
def check_answer(guess:str):
|
206 |
+
global guesses
|
207 |
+
global answer
|
208 |
+
guesses.append(guess)
|
209 |
+
output = ""
|
210 |
+
for guess in guesses:
|
211 |
+
output += ("- " + guess + "\n")
|
212 |
+
output = output[:-1]
|
213 |
+
|
214 |
+
if guess.lower() == answer.lower():
|
215 |
+
return "Correct!", output
|
216 |
+
else:
|
217 |
+
return "Try again!", output
|
218 |
+
|
219 |
+
def main():
|
220 |
+
print("BEGIN")
|
221 |
+
word1 = "Black"
|
222 |
+
word2 = "White"
|
223 |
+
word3 = "Sun"
|
224 |
+
global answer
|
225 |
+
answer = "Moon"
|
226 |
+
global guesses
|
227 |
+
|
228 |
+
training()
|
229 |
+
|
230 |
+
|
231 |
+
|
232 |
+
|
233 |
+
|
234 |
+
if __name__ == "__main__":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
235 |
main()
|
results/checkpoint-16000/added_tokens.json
ADDED
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"<extra_id_0>": 32099,
|
3 |
+
"<extra_id_10>": 32089,
|
4 |
+
"<extra_id_11>": 32088,
|
5 |
+
"<extra_id_12>": 32087,
|
6 |
+
"<extra_id_13>": 32086,
|
7 |
+
"<extra_id_14>": 32085,
|
8 |
+
"<extra_id_15>": 32084,
|
9 |
+
"<extra_id_16>": 32083,
|
10 |
+
"<extra_id_17>": 32082,
|
11 |
+
"<extra_id_18>": 32081,
|
12 |
+
"<extra_id_19>": 32080,
|
13 |
+
"<extra_id_1>": 32098,
|
14 |
+
"<extra_id_20>": 32079,
|
15 |
+
"<extra_id_21>": 32078,
|
16 |
+
"<extra_id_22>": 32077,
|
17 |
+
"<extra_id_23>": 32076,
|
18 |
+
"<extra_id_24>": 32075,
|
19 |
+
"<extra_id_25>": 32074,
|
20 |
+
"<extra_id_26>": 32073,
|
21 |
+
"<extra_id_27>": 32072,
|
22 |
+
"<extra_id_28>": 32071,
|
23 |
+
"<extra_id_29>": 32070,
|
24 |
+
"<extra_id_2>": 32097,
|
25 |
+
"<extra_id_30>": 32069,
|
26 |
+
"<extra_id_31>": 32068,
|
27 |
+
"<extra_id_32>": 32067,
|
28 |
+
"<extra_id_33>": 32066,
|
29 |
+
"<extra_id_34>": 32065,
|
30 |
+
"<extra_id_35>": 32064,
|
31 |
+
"<extra_id_36>": 32063,
|
32 |
+
"<extra_id_37>": 32062,
|
33 |
+
"<extra_id_38>": 32061,
|
34 |
+
"<extra_id_39>": 32060,
|
35 |
+
"<extra_id_3>": 32096,
|
36 |
+
"<extra_id_40>": 32059,
|
37 |
+
"<extra_id_41>": 32058,
|
38 |
+
"<extra_id_42>": 32057,
|
39 |
+
"<extra_id_43>": 32056,
|
40 |
+
"<extra_id_44>": 32055,
|
41 |
+
"<extra_id_45>": 32054,
|
42 |
+
"<extra_id_46>": 32053,
|
43 |
+
"<extra_id_47>": 32052,
|
44 |
+
"<extra_id_48>": 32051,
|
45 |
+
"<extra_id_49>": 32050,
|
46 |
+
"<extra_id_4>": 32095,
|
47 |
+
"<extra_id_50>": 32049,
|
48 |
+
"<extra_id_51>": 32048,
|
49 |
+
"<extra_id_52>": 32047,
|
50 |
+
"<extra_id_53>": 32046,
|
51 |
+
"<extra_id_54>": 32045,
|
52 |
+
"<extra_id_55>": 32044,
|
53 |
+
"<extra_id_56>": 32043,
|
54 |
+
"<extra_id_57>": 32042,
|
55 |
+
"<extra_id_58>": 32041,
|
56 |
+
"<extra_id_59>": 32040,
|
57 |
+
"<extra_id_5>": 32094,
|
58 |
+
"<extra_id_60>": 32039,
|
59 |
+
"<extra_id_61>": 32038,
|
60 |
+
"<extra_id_62>": 32037,
|
61 |
+
"<extra_id_63>": 32036,
|
62 |
+
"<extra_id_64>": 32035,
|
63 |
+
"<extra_id_65>": 32034,
|
64 |
+
"<extra_id_66>": 32033,
|
65 |
+
"<extra_id_67>": 32032,
|
66 |
+
"<extra_id_68>": 32031,
|
67 |
+
"<extra_id_69>": 32030,
|
68 |
+
"<extra_id_6>": 32093,
|
69 |
+
"<extra_id_70>": 32029,
|
70 |
+
"<extra_id_71>": 32028,
|
71 |
+
"<extra_id_72>": 32027,
|
72 |
+
"<extra_id_73>": 32026,
|
73 |
+
"<extra_id_74>": 32025,
|
74 |
+
"<extra_id_75>": 32024,
|
75 |
+
"<extra_id_76>": 32023,
|
76 |
+
"<extra_id_77>": 32022,
|
77 |
+
"<extra_id_78>": 32021,
|
78 |
+
"<extra_id_79>": 32020,
|
79 |
+
"<extra_id_7>": 32092,
|
80 |
+
"<extra_id_80>": 32019,
|
81 |
+
"<extra_id_81>": 32018,
|
82 |
+
"<extra_id_82>": 32017,
|
83 |
+
"<extra_id_83>": 32016,
|
84 |
+
"<extra_id_84>": 32015,
|
85 |
+
"<extra_id_85>": 32014,
|
86 |
+
"<extra_id_86>": 32013,
|
87 |
+
"<extra_id_87>": 32012,
|
88 |
+
"<extra_id_88>": 32011,
|
89 |
+
"<extra_id_89>": 32010,
|
90 |
+
"<extra_id_8>": 32091,
|
91 |
+
"<extra_id_90>": 32009,
|
92 |
+
"<extra_id_91>": 32008,
|
93 |
+
"<extra_id_92>": 32007,
|
94 |
+
"<extra_id_93>": 32006,
|
95 |
+
"<extra_id_94>": 32005,
|
96 |
+
"<extra_id_95>": 32004,
|
97 |
+
"<extra_id_96>": 32003,
|
98 |
+
"<extra_id_97>": 32002,
|
99 |
+
"<extra_id_98>": 32001,
|
100 |
+
"<extra_id_99>": 32000,
|
101 |
+
"<extra_id_9>": 32090
|
102 |
+
}
|
results/checkpoint-16000/config.json
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "google/flan-t5-base",
|
3 |
+
"architectures": [
|
4 |
+
"T5ForConditionalGeneration"
|
5 |
+
],
|
6 |
+
"classifier_dropout": 0.0,
|
7 |
+
"d_ff": 2048,
|
8 |
+
"d_kv": 64,
|
9 |
+
"d_model": 768,
|
10 |
+
"decoder_start_token_id": 0,
|
11 |
+
"dense_act_fn": "gelu_new",
|
12 |
+
"dropout_rate": 0.1,
|
13 |
+
"eos_token_id": 1,
|
14 |
+
"feed_forward_proj": "gated-gelu",
|
15 |
+
"initializer_factor": 1.0,
|
16 |
+
"is_encoder_decoder": true,
|
17 |
+
"is_gated_act": true,
|
18 |
+
"layer_norm_epsilon": 1e-06,
|
19 |
+
"model_type": "t5",
|
20 |
+
"n_positions": 512,
|
21 |
+
"num_decoder_layers": 12,
|
22 |
+
"num_heads": 12,
|
23 |
+
"num_layers": 12,
|
24 |
+
"output_past": true,
|
25 |
+
"pad_token_id": 0,
|
26 |
+
"relative_attention_max_distance": 128,
|
27 |
+
"relative_attention_num_buckets": 32,
|
28 |
+
"task_specific_params": {
|
29 |
+
"summarization": {
|
30 |
+
"early_stopping": true,
|
31 |
+
"length_penalty": 2.0,
|
32 |
+
"max_length": 200,
|
33 |
+
"min_length": 30,
|
34 |
+
"no_repeat_ngram_size": 3,
|
35 |
+
"num_beams": 4,
|
36 |
+
"prefix": "summarize: "
|
37 |
+
},
|
38 |
+
"translation_en_to_de": {
|
39 |
+
"early_stopping": true,
|
40 |
+
"max_length": 300,
|
41 |
+
"num_beams": 4,
|
42 |
+
"prefix": "translate English to German: "
|
43 |
+
},
|
44 |
+
"translation_en_to_fr": {
|
45 |
+
"early_stopping": true,
|
46 |
+
"max_length": 300,
|
47 |
+
"num_beams": 4,
|
48 |
+
"prefix": "translate English to French: "
|
49 |
+
},
|
50 |
+
"translation_en_to_ro": {
|
51 |
+
"early_stopping": true,
|
52 |
+
"max_length": 300,
|
53 |
+
"num_beams": 4,
|
54 |
+
"prefix": "translate English to Romanian: "
|
55 |
+
}
|
56 |
+
},
|
57 |
+
"tie_word_embeddings": false,
|
58 |
+
"torch_dtype": "float32",
|
59 |
+
"transformers_version": "4.35.2",
|
60 |
+
"use_cache": true,
|
61 |
+
"vocab_size": 32128
|
62 |
+
}
|
results/checkpoint-16000/generation_config.json
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"decoder_start_token_id": 0,
|
3 |
+
"eos_token_id": 1,
|
4 |
+
"pad_token_id": 0,
|
5 |
+
"transformers_version": "4.35.2"
|
6 |
+
}
|
results/checkpoint-16000/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cd7f96db75733e18d6af8488ab51eea991be641c6c22b24fa5ab3b45101c3398
|
3 |
+
size 990345064
|
results/checkpoint-16000/optimizer.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:31aa07bcfc63b03b9dbfb77536457e4d0591b64d537e2f4834f5b81c6bd2ab21
|
3 |
+
size 1980860410
|
results/checkpoint-16000/rng_state.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cc296e1811c88d4548bfa74b8cf96485e58c41652ba8a0db69b6e3a9762f9be0
|
3 |
+
size 14244
|
results/checkpoint-16000/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8c77d751bb87ca04afd8f823ee9102cffea6221900b1a056c2f31d9044f1a0ce
|
3 |
+
size 1064
|
results/checkpoint-16000/special_tokens_map.json
ADDED
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<extra_id_0>",
|
4 |
+
"<extra_id_1>",
|
5 |
+
"<extra_id_2>",
|
6 |
+
"<extra_id_3>",
|
7 |
+
"<extra_id_4>",
|
8 |
+
"<extra_id_5>",
|
9 |
+
"<extra_id_6>",
|
10 |
+
"<extra_id_7>",
|
11 |
+
"<extra_id_8>",
|
12 |
+
"<extra_id_9>",
|
13 |
+
"<extra_id_10>",
|
14 |
+
"<extra_id_11>",
|
15 |
+
"<extra_id_12>",
|
16 |
+
"<extra_id_13>",
|
17 |
+
"<extra_id_14>",
|
18 |
+
"<extra_id_15>",
|
19 |
+
"<extra_id_16>",
|
20 |
+
"<extra_id_17>",
|
21 |
+
"<extra_id_18>",
|
22 |
+
"<extra_id_19>",
|
23 |
+
"<extra_id_20>",
|
24 |
+
"<extra_id_21>",
|
25 |
+
"<extra_id_22>",
|
26 |
+
"<extra_id_23>",
|
27 |
+
"<extra_id_24>",
|
28 |
+
"<extra_id_25>",
|
29 |
+
"<extra_id_26>",
|
30 |
+
"<extra_id_27>",
|
31 |
+
"<extra_id_28>",
|
32 |
+
"<extra_id_29>",
|
33 |
+
"<extra_id_30>",
|
34 |
+
"<extra_id_31>",
|
35 |
+
"<extra_id_32>",
|
36 |
+
"<extra_id_33>",
|
37 |
+
"<extra_id_34>",
|
38 |
+
"<extra_id_35>",
|
39 |
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"<extra_id_36>",
|
40 |
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"<extra_id_37>",
|
41 |
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"<extra_id_38>",
|
42 |
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"<extra_id_39>",
|
43 |
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"<extra_id_40>",
|
44 |
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"<extra_id_41>",
|
45 |
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"<extra_id_42>",
|
46 |
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|
47 |
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"<extra_id_44>",
|
48 |
+
"<extra_id_45>",
|
49 |
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"<extra_id_46>",
|
50 |
+
"<extra_id_47>",
|
51 |
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"<extra_id_48>",
|
52 |
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"<extra_id_49>",
|
53 |
+
"<extra_id_50>",
|
54 |
+
"<extra_id_51>",
|
55 |
+
"<extra_id_52>",
|
56 |
+
"<extra_id_53>",
|
57 |
+
"<extra_id_54>",
|
58 |
+
"<extra_id_55>",
|
59 |
+
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|
60 |
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|
61 |
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|
62 |
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|
63 |
+
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|
64 |
+
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|
65 |
+
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|
66 |
+
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|
67 |
+
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|
68 |
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|
69 |
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|
70 |
+
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|
71 |
+
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|
72 |
+
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|
73 |
+
"<extra_id_70>",
|
74 |
+
"<extra_id_71>",
|
75 |
+
"<extra_id_72>",
|
76 |
+
"<extra_id_73>",
|
77 |
+
"<extra_id_74>",
|
78 |
+
"<extra_id_75>",
|
79 |
+
"<extra_id_76>",
|
80 |
+
"<extra_id_77>",
|
81 |
+
"<extra_id_78>",
|
82 |
+
"<extra_id_79>",
|
83 |
+
"<extra_id_80>",
|
84 |
+
"<extra_id_81>",
|
85 |
+
"<extra_id_82>",
|
86 |
+
"<extra_id_83>",
|
87 |
+
"<extra_id_84>",
|
88 |
+
"<extra_id_85>",
|
89 |
+
"<extra_id_86>",
|
90 |
+
"<extra_id_87>",
|
91 |
+
"<extra_id_88>",
|
92 |
+
"<extra_id_89>",
|
93 |
+
"<extra_id_90>",
|
94 |
+
"<extra_id_91>",
|
95 |
+
"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
98 |
+
"<extra_id_95>",
|
99 |
+
"<extra_id_96>",
|
100 |
+
"<extra_id_97>",
|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": {
|
105 |
+
"content": "</s>",
|
106 |
+
"lstrip": false,
|
107 |
+
"normalized": false,
|
108 |
+
"rstrip": false,
|
109 |
+
"single_word": false
|
110 |
+
},
|
111 |
+
"pad_token": {
|
112 |
+
"content": "<pad>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false
|
117 |
+
},
|
118 |
+
"unk_token": {
|
119 |
+
"content": "<unk>",
|
120 |
+
"lstrip": false,
|
121 |
+
"normalized": false,
|
122 |
+
"rstrip": false,
|
123 |
+
"single_word": false
|
124 |
+
}
|
125 |
+
}
|
results/checkpoint-16000/spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
results/checkpoint-16000/tokenizer_config.json
ADDED
@@ -0,0 +1,939 @@
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|
1 |
+
{
|
2 |
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54 |
+
"<extra_id_51>",
|
55 |
+
"<extra_id_52>",
|
56 |
+
"<extra_id_53>",
|
57 |
+
"<extra_id_54>",
|
58 |
+
"<extra_id_55>",
|
59 |
+
"<extra_id_56>",
|
60 |
+
"<extra_id_57>",
|
61 |
+
"<extra_id_58>",
|
62 |
+
"<extra_id_59>",
|
63 |
+
"<extra_id_60>",
|
64 |
+
"<extra_id_61>",
|
65 |
+
"<extra_id_62>",
|
66 |
+
"<extra_id_63>",
|
67 |
+
"<extra_id_64>",
|
68 |
+
"<extra_id_65>",
|
69 |
+
"<extra_id_66>",
|
70 |
+
"<extra_id_67>",
|
71 |
+
"<extra_id_68>",
|
72 |
+
"<extra_id_69>",
|
73 |
+
"<extra_id_70>",
|
74 |
+
"<extra_id_71>",
|
75 |
+
"<extra_id_72>",
|
76 |
+
"<extra_id_73>",
|
77 |
+
"<extra_id_74>",
|
78 |
+
"<extra_id_75>",
|
79 |
+
"<extra_id_76>",
|
80 |
+
"<extra_id_77>",
|
81 |
+
"<extra_id_78>",
|
82 |
+
"<extra_id_79>",
|
83 |
+
"<extra_id_80>",
|
84 |
+
"<extra_id_81>",
|
85 |
+
"<extra_id_82>",
|
86 |
+
"<extra_id_83>",
|
87 |
+
"<extra_id_84>",
|
88 |
+
"<extra_id_85>",
|
89 |
+
"<extra_id_86>",
|
90 |
+
"<extra_id_87>",
|
91 |
+
"<extra_id_88>",
|
92 |
+
"<extra_id_89>",
|
93 |
+
"<extra_id_90>",
|
94 |
+
"<extra_id_91>",
|
95 |
+
"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
98 |
+
"<extra_id_95>",
|
99 |
+
"<extra_id_96>",
|
100 |
+
"<extra_id_97>",
|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": {
|
105 |
+
"content": "</s>",
|
106 |
+
"lstrip": false,
|
107 |
+
"normalized": false,
|
108 |
+
"rstrip": false,
|
109 |
+
"single_word": false
|
110 |
+
},
|
111 |
+
"pad_token": {
|
112 |
+
"content": "<pad>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false
|
117 |
+
},
|
118 |
+
"unk_token": {
|
119 |
+
"content": "<unk>",
|
120 |
+
"lstrip": false,
|
121 |
+
"normalized": false,
|
122 |
+
"rstrip": false,
|
123 |
+
"single_word": false
|
124 |
+
}
|
125 |
+
}
|
results/checkpoint-16500/spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
results/checkpoint-16500/tokenizer_config.json
ADDED
@@ -0,0 +1,939 @@
|
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|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "<pad>",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "</s>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "<unk>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"32000": {
|
28 |
+
"content": "<extra_id_99>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"32001": {
|
36 |
+
"content": "<extra_id_98>",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"32002": {
|
44 |
+
"content": "<extra_id_97>",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"32003": {
|
52 |
+
"content": "<extra_id_96>",
|
53 |
+
"lstrip": false,
|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
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894 |
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896 |
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898 |
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900 |
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|
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|
902 |
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903 |
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904 |
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|
905 |
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|
906 |
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|
907 |
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908 |
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|
909 |
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|
910 |
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|
911 |
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|
912 |
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|
913 |
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|
914 |
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|
915 |
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|
916 |
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|
917 |
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|
918 |
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|
919 |
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|
920 |
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|
921 |
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|
922 |
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"<extra_id_93>",
|
923 |
+
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|
924 |
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|
925 |
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|
926 |
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"<extra_id_97>",
|
927 |
+
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|
928 |
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"<extra_id_99>"
|
929 |
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|
930 |
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|
931 |
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|
932 |
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"extra_ids": 100,
|
933 |
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"legacy": true,
|
934 |
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|
935 |
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"pad_token": "<pad>",
|
936 |
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"sp_model_kwargs": {},
|
937 |
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"tokenizer_class": "T5Tokenizer",
|
938 |
+
"unk_token": "<unk>"
|
939 |
+
}
|
results/checkpoint-16500/trainer_state.json
ADDED
@@ -0,0 +1,325 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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results/checkpoint-17000/added_tokens.json
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@@ -0,0 +1,102 @@
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{
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3 |
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"<extra_id_84>": 32015,
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|
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|
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|
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|
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|
102 |
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}
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results/checkpoint-17000/config.json
ADDED
@@ -0,0 +1,62 @@
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1 |
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{
|
2 |
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"_name_or_path": "google/flan-t5-base",
|
3 |
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"architectures": [
|
4 |
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"T5ForConditionalGeneration"
|
5 |
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],
|
6 |
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"classifier_dropout": 0.0,
|
7 |
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"d_ff": 2048,
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"d_kv": 64,
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"d_model": 768,
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"dense_act_fn": "gelu_new",
|
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
|
15 |
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"initializer_factor": 1.0,
|
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"is_encoder_decoder": true,
|
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"is_gated_act": true,
|
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
|
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"n_positions": 512,
|
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"num_decoder_layers": 12,
|
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"num_heads": 12,
|
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
|
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"relative_attention_num_buckets": 32,
|
28 |
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"task_specific_params": {
|
29 |
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"summarization": {
|
30 |
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"early_stopping": true,
|
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"length_penalty": 2.0,
|
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"max_length": 200,
|
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"min_length": 30,
|
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"no_repeat_ngram_size": 3,
|
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"num_beams": 4,
|
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"prefix": "summarize: "
|
37 |
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},
|
38 |
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"translation_en_to_de": {
|
39 |
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"early_stopping": true,
|
40 |
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"max_length": 300,
|
41 |
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"num_beams": 4,
|
42 |
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"prefix": "translate English to German: "
|
43 |
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},
|
44 |
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"translation_en_to_fr": {
|
45 |
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"early_stopping": true,
|
46 |
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"max_length": 300,
|
47 |
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"num_beams": 4,
|
48 |
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"prefix": "translate English to French: "
|
49 |
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},
|
50 |
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"translation_en_to_ro": {
|
51 |
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"early_stopping": true,
|
52 |
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"max_length": 300,
|
53 |
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"num_beams": 4,
|
54 |
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"prefix": "translate English to Romanian: "
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55 |
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}
|
56 |
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},
|
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"tie_word_embeddings": false,
|
58 |
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"torch_dtype": "float32",
|
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"transformers_version": "4.35.2",
|
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"use_cache": true,
|
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"vocab_size": 32128
|
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}
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results/checkpoint-17000/generation_config.json
ADDED
@@ -0,0 +1,6 @@
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{
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.35.2"
|
6 |
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}
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results/checkpoint-17000/model.safetensors
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:92fb7ee142103a1cb7adb1d571589e7d21d7239f2e1cb7ca9a6b33c506c487ea
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3 |
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size 990345064
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results/checkpoint-17000/optimizer.pt
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:6fe36b8f5c0d0cd2fb3db9f24cd099ee0a5ac33700d73b159bcfb743c7fb4257
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size 1980860410
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results/checkpoint-17000/rng_state.pth
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1276a1a4eea6d9d0454dcea4e04dda05b3562ae9183eaf21b7cce953d6a88e2
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size 14244
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results/checkpoint-17000/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3992cc3c175d24af106b82e4a70c8b2654ca5720363a954f8a160d3ed6a680f
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size 1064
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results/checkpoint-17000/special_tokens_map.json
ADDED
@@ -0,0 +1,125 @@
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{
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"<extra_id_89>",
|
93 |
+
"<extra_id_90>",
|
94 |
+
"<extra_id_91>",
|
95 |
+
"<extra_id_92>",
|
96 |
+
"<extra_id_93>",
|
97 |
+
"<extra_id_94>",
|
98 |
+
"<extra_id_95>",
|
99 |
+
"<extra_id_96>",
|
100 |
+
"<extra_id_97>",
|
101 |
+
"<extra_id_98>",
|
102 |
+
"<extra_id_99>"
|
103 |
+
],
|
104 |
+
"eos_token": {
|
105 |
+
"content": "</s>",
|
106 |
+
"lstrip": false,
|
107 |
+
"normalized": false,
|
108 |
+
"rstrip": false,
|
109 |
+
"single_word": false
|
110 |
+
},
|
111 |
+
"pad_token": {
|
112 |
+
"content": "<pad>",
|
113 |
+
"lstrip": false,
|
114 |
+
"normalized": false,
|
115 |
+
"rstrip": false,
|
116 |
+
"single_word": false
|
117 |
+
},
|
118 |
+
"unk_token": {
|
119 |
+
"content": "<unk>",
|
120 |
+
"lstrip": false,
|
121 |
+
"normalized": false,
|
122 |
+
"rstrip": false,
|
123 |
+
"single_word": false
|
124 |
+
}
|
125 |
+
}
|
results/checkpoint-17000/spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
results/checkpoint-17000/tokenizer_config.json
ADDED
@@ -0,0 +1,939 @@
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|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "<pad>",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "</s>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "<unk>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"32000": {
|
28 |
+
"content": "<extra_id_99>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"32001": {
|
36 |
+
"content": "<extra_id_98>",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"32002": {
|
44 |
+
"content": "<extra_id_97>",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"32003": {
|
52 |
+
"content": "<extra_id_96>",
|
53 |
+
"lstrip": false,
|
54 |
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"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
"single_word": false,
|
57 |
+
"special": true
|
58 |
+
},
|
59 |
+
"32004": {
|
60 |
+
"content": "<extra_id_95>",
|
61 |
+
"lstrip": false,
|
62 |
+
"normalized": false,
|
63 |
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"rstrip": false,
|
64 |
+
"single_word": false,
|
65 |
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"special": true
|
66 |
+
},
|
67 |
+
"32005": {
|
68 |
+
"content": "<extra_id_94>",
|
69 |
+
"lstrip": false,
|
70 |
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"normalized": false,
|
71 |
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"rstrip": false,
|
72 |
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"single_word": false,
|
73 |
+
"special": true
|
74 |
+
},
|
75 |
+
"32006": {
|
76 |
+
"content": "<extra_id_93>",
|
77 |
+
"lstrip": false,
|
78 |
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"normalized": false,
|
79 |
+
"rstrip": false,
|
80 |
+
"single_word": false,
|
81 |
+
"special": true
|
82 |
+
},
|
83 |
+
"32007": {
|
84 |
+
"content": "<extra_id_92>",
|
85 |
+
"lstrip": false,
|
86 |
+
"normalized": false,
|
87 |
+
"rstrip": false,
|
88 |
+
"single_word": false,
|
89 |
+
"special": true
|
90 |
+
},
|
91 |
+
"32008": {
|
92 |
+
"content": "<extra_id_91>",
|
93 |
+
"lstrip": false,
|
94 |
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"normalized": false,
|
95 |
+
"rstrip": false,
|
96 |
+
"single_word": false,
|
97 |
+
"special": true
|
98 |
+
},
|
99 |
+
"32009": {
|
100 |
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|
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929 |
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930 |
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|
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937 |
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|
938 |
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"unk_token": "<unk>"
|
939 |
+
}
|
results/checkpoint-17000/trainer_state.json
ADDED
@@ -0,0 +1,331 @@
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word_embedding.py
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|
617 |
main()
|
|
|
1 |
+
<<<<<<< HEAD
|
2 |
from datasets import load_dataset
|
3 |
import shutil
|
4 |
import json
|
|
|
615 |
|
616 |
|
617 |
if __name__ == "__main__":
|
618 |
+
=======
|
619 |
+
from datasets import load_dataset
|
620 |
+
import shutil
|
621 |
+
import json
|
622 |
+
from collections import defaultdict
|
623 |
+
import multiprocessing
|
624 |
+
import gensim
|
625 |
+
from sklearn.metrics import classification_report
|
626 |
+
from gensim import corpora
|
627 |
+
from gensim.test.utils import common_texts
|
628 |
+
from gensim.models import Word2Vec
|
629 |
+
from gensim.models import KeyedVectors
|
630 |
+
from gensim.models import fasttext
|
631 |
+
from gensim.test.utils import datapath
|
632 |
+
from wefe.datasets import load_bingliu
|
633 |
+
from wefe.metrics import RNSB
|
634 |
+
from wefe.query import Query
|
635 |
+
from wefe.word_embedding_model import WordEmbeddingModel
|
636 |
+
from wefe.utils import plot_queries_results, run_queries
|
637 |
+
import pandas as pd
|
638 |
+
import gensim.downloader as api
|
639 |
+
import glob
|
640 |
+
from sklearn.feature_extraction.text import TfidfVectorizer
|
641 |
+
from sklearn.ensemble import RandomForestClassifier
|
642 |
+
from wefe.metrics import WEAT
|
643 |
+
from wefe.datasets import load_weat
|
644 |
+
from wefe.utils import run_queries
|
645 |
+
from wefe.utils import plot_queries_results
|
646 |
+
import random
|
647 |
+
from scipy.special import expit
|
648 |
+
import math
|
649 |
+
import sys
|
650 |
+
import os
|
651 |
+
import argparse
|
652 |
+
import nltk
|
653 |
+
import scipy.sparse
|
654 |
+
import numpy as np
|
655 |
+
import string
|
656 |
+
import io
|
657 |
+
from sklearn.model_selection import train_test_split
|
658 |
+
|
659 |
+
|
660 |
+
'''STEPS FOR CODE:
|
661 |
+
1. Train word embeddings on Simple English Wikipedia;
|
662 |
+
2. Compare these to other pre-trained embeddings;
|
663 |
+
3. Quantify biases that exist in these word embeddings;
|
664 |
+
4. Use your word embeddings as features in a simple text classifier;
|
665 |
+
'''
|
666 |
+
|
667 |
+
|
668 |
+
def load_vectors(fname):
|
669 |
+
fin = io.open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
|
670 |
+
n, d = map(int, fin.readline().split())
|
671 |
+
data = {}
|
672 |
+
# print("Hello", n, d)
|
673 |
+
for line in fin:
|
674 |
+
tokens = line.rstrip().split(' ')
|
675 |
+
data[tokens[0]] = map(float, tokens[1:])
|
676 |
+
# print(data)
|
677 |
+
|
678 |
+
print(data)
|
679 |
+
return data
|
680 |
+
|
681 |
+
|
682 |
+
def train_embeddings():
|
683 |
+
'''TRAIN WORD EMBEDDINGS
|
684 |
+
This will be making use of the dataset from wikipedia and the first step'''
|
685 |
+
dataset = load_dataset("wikipedia", "20220301.simple")
|
686 |
+
cores = multiprocessing.cpu_count()
|
687 |
+
# check the first example of the training portion of the dataset :
|
688 |
+
# print(dataset['train'][0])
|
689 |
+
dataset_size = len(dataset)
|
690 |
+
|
691 |
+
### BUILD VOCAB ###
|
692 |
+
# print(type(dataset["train"][0]))
|
693 |
+
vocab = set()
|
694 |
+
vocab_size = 0
|
695 |
+
count = 0
|
696 |
+
## Generate vocab and split sentances and words?
|
697 |
+
data = []
|
698 |
+
for index, page in enumerate(dataset["train"]):
|
699 |
+
document = page["text"]
|
700 |
+
document = document.replace("\n", ". ")
|
701 |
+
# print(document)
|
702 |
+
for sent in document.split("."):
|
703 |
+
# print("Sentance:", sent)
|
704 |
+
new_sent = []
|
705 |
+
clean_sent =[s for s in sent if s.isalnum() or s.isspace()]
|
706 |
+
clean_sent = "".join(clean_sent)
|
707 |
+
for word in clean_sent.split(" "):
|
708 |
+
if len(word) > 0:
|
709 |
+
new_word = word.lower()
|
710 |
+
# print("Word:", new_word)
|
711 |
+
if new_word[0] not in string.punctuation:
|
712 |
+
new_sent.append(new_word)
|
713 |
+
if len(new_sent) > 0:
|
714 |
+
data.append(new_sent)
|
715 |
+
# print("New Sent:", new_sent)
|
716 |
+
|
717 |
+
|
718 |
+
for index, page in enumerate(dataset["train"]):
|
719 |
+
# print(page["text"])
|
720 |
+
# for text in page:
|
721 |
+
# print(text)
|
722 |
+
text = page["text"]
|
723 |
+
clean_text = [s for s in text if s.isalnum() or s.isspace()]
|
724 |
+
clean_text = "".join(clean_text)
|
725 |
+
clean_text = clean_text.replace("\n", " ")
|
726 |
+
# text = text.replace('; ', ' ').replace(", ", " ").replace("\n", " ").replace(":", " ").replace(". ", " ").replace("! ", " ").replace("? ", " ").replace()
|
727 |
+
|
728 |
+
for word in clean_text.split(" "):
|
729 |
+
# print(word)
|
730 |
+
if word != "\n" and word != " " and word not in vocab:
|
731 |
+
vocab.add(word)
|
732 |
+
vocab_size += 1
|
733 |
+
# if index == 10:
|
734 |
+
# break
|
735 |
+
# print(f"word #{index}/{count} is {word}")
|
736 |
+
count += 1
|
737 |
+
|
738 |
+
# print(f"There are {vocab_size} vocab words")
|
739 |
+
|
740 |
+
embeddings_model = Word2Vec(
|
741 |
+
data,
|
742 |
+
epochs= 10,
|
743 |
+
window=10,
|
744 |
+
vector_size= 50)
|
745 |
+
embeddings_model.save("word2vec.model")
|
746 |
+
|
747 |
+
skip_model = Word2Vec(
|
748 |
+
data,
|
749 |
+
epochs= 10,
|
750 |
+
window=10,
|
751 |
+
vector_size= 50,
|
752 |
+
sg=1)
|
753 |
+
skip_model.save("skip2vec.model")
|
754 |
+
|
755 |
+
embeddings_model = Word2Vec.load("word2vec.model")
|
756 |
+
skip_model = Word2Vec.load("skip2vec.model")
|
757 |
+
|
758 |
+
# embeddings_model.train(dataset, total_examples=dataset_size, epochs=15)
|
759 |
+
# print(embeddings_model['train'])
|
760 |
+
# print(embeddings_model.wv["france"])
|
761 |
+
return embeddings_model, skip_model
|
762 |
+
|
763 |
+
|
764 |
+
def get_data():
|
765 |
+
dataset = load_dataset("wikipedia", "20220301.simple")
|
766 |
+
cores = multiprocessing.cpu_count()
|
767 |
+
# check the first example of the training portion of the dataset :
|
768 |
+
# print(dataset['train'][0])
|
769 |
+
dataset_size = len(dataset)
|
770 |
+
|
771 |
+
### BUILD VOCAB ###
|
772 |
+
# print(type(dataset["train"][0]))
|
773 |
+
vocab = set()
|
774 |
+
vocab_size = 0
|
775 |
+
count = 0
|
776 |
+
## Generate vocab and split sentances and words?
|
777 |
+
data = []
|
778 |
+
num_sents = 0
|
779 |
+
for index, page in enumerate(dataset["train"]):
|
780 |
+
document = page["text"]
|
781 |
+
document = document.replace("\n", ". ")
|
782 |
+
# print(document)
|
783 |
+
for sent in document.split("."):
|
784 |
+
num_sents += 1
|
785 |
+
# print("Sentance:", sent)
|
786 |
+
new_sent = []
|
787 |
+
clean_sent =[s for s in sent if s.isalnum() or s.isspace()]
|
788 |
+
clean_sent = "".join(clean_sent)
|
789 |
+
for word in clean_sent.split(" "):
|
790 |
+
if len(word) > 0:
|
791 |
+
new_word = word.lower()
|
792 |
+
# print("Word:", new_word)
|
793 |
+
if new_word[0] not in string.punctuation:
|
794 |
+
new_sent.append(new_word)
|
795 |
+
if len(new_sent) > 0:
|
796 |
+
data.append(new_sent)
|
797 |
+
# print("New Sent:", new_sent)
|
798 |
+
|
799 |
+
return data, num_sents
|
800 |
+
|
801 |
+
|
802 |
+
def compare_embeddings(cbow, skip, urban, fasttext):
|
803 |
+
'''COMPARE EMBEDDINGS'''
|
804 |
+
print("Most Similar to dog")
|
805 |
+
print("cbow", cbow.wv.most_similar(positive=['dog'], negative=[], topn=2))
|
806 |
+
print("skip", skip.wv.most_similar(positive=['dog'], negative=[], topn=2))
|
807 |
+
print("urban", urban.most_similar(positive=['dog'], negative=[], topn=2))
|
808 |
+
print("fasttext", fasttext.most_similar(positive=['dog'], negative=[], topn=2))
|
809 |
+
|
810 |
+
print("\nMost Similar to Pizza - Pepperoni + Pretzel")
|
811 |
+
print("cbow", cbow.wv.most_similar(positive=['pizza', 'pretzel'], negative=['pepperoni'], topn=2))
|
812 |
+
print("skip", skip.wv.most_similar(positive=['pizza', 'pretzel'], negative=['pepperoni'], topn=2))
|
813 |
+
print("urban", urban.most_similar(positive=['pizza', 'pretzel'], negative=['pepperoni'], topn=2))
|
814 |
+
print("fasttext", fasttext.most_similar(positive=['pizza', 'pretzel'], negative=['pepperoni'], topn=2))
|
815 |
+
|
816 |
+
print("\nMost Similar to witch - woman + man")
|
817 |
+
print("cbow", cbow.wv.most_similar(positive=['witch', 'man'], negative=['woman'], topn=2))
|
818 |
+
print("skip", skip.wv.most_similar(positive=['witch', 'man'], negative=['woman'], topn=2))
|
819 |
+
print("urban", urban.most_similar(positive=['witch', 'man'], negative=['woman'], topn=2))
|
820 |
+
print("fasttext", fasttext.most_similar(positive=['witch', 'man'], negative=['woman'], topn=2))
|
821 |
+
|
822 |
+
print("\nMost Similar to mayor - town + country")
|
823 |
+
print("cbow", cbow.wv.most_similar(positive=['mayor', 'country'], negative=['town'], topn=2))
|
824 |
+
print("skip", skip.wv.most_similar(positive=['mayor', 'country'], negative=['town'], topn=2))
|
825 |
+
print("urban", urban.most_similar(positive=['mayor', 'country'], negative=['town'], topn=2))
|
826 |
+
print("fasttext", fasttext.most_similar(positive=['mayor', 'country'], negative=['town'], topn=2))
|
827 |
+
|
828 |
+
print("\nMost Similar to death")
|
829 |
+
print("cbow", cbow.wv.most_similar(positive=['death'], negative=[], topn=2))
|
830 |
+
print("skip", skip.wv.most_similar(positive=['death'], negative=[], topn=2))
|
831 |
+
print("urban", urban.most_similar(positive=['death'], negative=[], topn=2))
|
832 |
+
print("fasttext", fasttext.most_similar(positive=['death'], negative=[], topn=2))
|
833 |
+
|
834 |
+
|
835 |
+
def quantify_bias(cbow, skip, urban, fasttext):
|
836 |
+
'''QUANTIFY BIASES'''
|
837 |
+
'''Using WEFE, RNSB'''
|
838 |
+
|
839 |
+
RNSB_words = [
|
840 |
+
['christianity'],
|
841 |
+
['catholicism'],
|
842 |
+
['islam'],
|
843 |
+
['judaism'],
|
844 |
+
['hinduism'],
|
845 |
+
['buddhism'],
|
846 |
+
['mormonism'],
|
847 |
+
['scientology'],
|
848 |
+
['taoism']]
|
849 |
+
|
850 |
+
weat_wordset = load_weat()
|
851 |
+
|
852 |
+
models = [WordEmbeddingModel(cbow.wv, "CBOW"),
|
853 |
+
WordEmbeddingModel(skip.wv, "skip-gram"),
|
854 |
+
WordEmbeddingModel(urban, "urban dictionary"),
|
855 |
+
WordEmbeddingModel(fasttext, "fasttext")]
|
856 |
+
|
857 |
+
# Define the 10 Queries:
|
858 |
+
# print(weat_wordset["science"])
|
859 |
+
religions = ['christianity',
|
860 |
+
'catholicism',
|
861 |
+
'islam',
|
862 |
+
'judaism',
|
863 |
+
'hinduism',
|
864 |
+
'buddhism',
|
865 |
+
'mormonism',
|
866 |
+
'scientology',
|
867 |
+
'taoism',
|
868 |
+
'atheism']
|
869 |
+
queries = [
|
870 |
+
# Flowers vs Insects wrt Pleasant (5) and Unpleasant (5)
|
871 |
+
Query([religions, weat_wordset['arts']],
|
872 |
+
[weat_wordset['career'], weat_wordset['family']],
|
873 |
+
['Religion', 'Art'], ['Career', 'Family']),
|
874 |
+
|
875 |
+
Query([religions, weat_wordset['weapons']],
|
876 |
+
[weat_wordset['male_terms'], weat_wordset['female_terms']],
|
877 |
+
['Religion', 'Weapons'], ['Male terms', 'Female terms']),
|
878 |
+
|
879 |
+
]
|
880 |
+
|
881 |
+
wefe_results = run_queries(WEAT,
|
882 |
+
queries,
|
883 |
+
models,
|
884 |
+
metric_params ={
|
885 |
+
'preprocessors': [
|
886 |
+
{},
|
887 |
+
{'lowercase': True }
|
888 |
+
]
|
889 |
+
},
|
890 |
+
warn_not_found_words = True
|
891 |
+
).T.round(2)
|
892 |
+
|
893 |
+
print(wefe_results)
|
894 |
+
plot_queries_results(wefe_results).show()
|
895 |
+
|
896 |
+
|
897 |
+
def text_classifier(cbow):
|
898 |
+
'''SIMPLE TEXT CLASSIFIER'''
|
899 |
+
'''For each document, average together all embeddings for the
|
900 |
+
individual words in that document to get a new, d-dimensional representation
|
901 |
+
of that document (this is essentially a “continuous bag-of-words”). Note that
|
902 |
+
your input feature size is only d now, instead of the size of your entire vocabulary.
|
903 |
+
Compare the results of training a model using these “CBOW” input features to
|
904 |
+
your original (discrete) BOW model.'''
|
905 |
+
pos_train_files = glob.glob('aclImdb/train/pos/*')
|
906 |
+
neg_train_files = glob.glob('aclImdb/train/neg/*')
|
907 |
+
# print(pos_train_files[:5])
|
908 |
+
|
909 |
+
num_files_per_class = 1000
|
910 |
+
# bow_train_files = cbow
|
911 |
+
all_train_files = pos_train_files[:num_files_per_class] + neg_train_files[:num_files_per_class]
|
912 |
+
# vectorizer = TfidfVectorizer(input="filename", stop_words="english")
|
913 |
+
# vectors = vectorizer.fit_transform(all_train_files)
|
914 |
+
d = len(cbow.wv["man"])
|
915 |
+
vectors = np.empty([len(all_train_files), d])
|
916 |
+
count = 0
|
917 |
+
vocab = set()
|
918 |
+
for doc in all_train_files:
|
919 |
+
temp_array = avg_embeddings(doc, cbow, vocab)
|
920 |
+
if len(temp_array) > 0:
|
921 |
+
vectors[count] = temp_array
|
922 |
+
count += 1
|
923 |
+
else:
|
924 |
+
vectors = np.delete(vectors, count)
|
925 |
+
# vectors = np.array(avg_embeddings(doc, cbow) for doc in all_train_files)
|
926 |
+
# print(vectors)
|
927 |
+
# print(vocab)
|
928 |
+
|
929 |
+
# len(vectorizer.vocabulary_)
|
930 |
+
vectors[0].sum()
|
931 |
+
# print("Vector at 0", vectors[0])
|
932 |
+
|
933 |
+
X = vectors
|
934 |
+
y = [1] * num_files_per_class + [0] * num_files_per_class
|
935 |
+
len(y)
|
936 |
+
|
937 |
+
x_0 = X[0]
|
938 |
+
w = np.zeros(X.shape[1])
|
939 |
+
# x_0_dense = x_0.todense()
|
940 |
+
x_0.dot(w)
|
941 |
+
|
942 |
+
w,b = sgd_for_lr_with_ce(X,y)
|
943 |
+
# w
|
944 |
+
|
945 |
+
# sorted_vocab = sorted([(k,v) for k,v in vectorizer.vocabulary_.items()],key=lambda x:x[1])
|
946 |
+
sorted_vocab = sorted(vocab)
|
947 |
+
# sorted_vocab = [a for (a,b) in sorted_vocab]
|
948 |
+
|
949 |
+
sorted_words_weights = sorted([x for x in zip(sorted_vocab, w)], key=lambda x:x[1])
|
950 |
+
sorted_words_weights[-50:]
|
951 |
+
|
952 |
+
preds = predict_y_lr(w,b,X)
|
953 |
+
|
954 |
+
preds
|
955 |
+
|
956 |
+
w,b = sgd_for_lr_with_ce(X, y, num_passes=10)
|
957 |
+
y_pred = predict_y_lr(w,b,X)
|
958 |
+
print(classification_report(y, y_pred))
|
959 |
+
|
960 |
+
# compute for dev set
|
961 |
+
# pos_dev_files = glob.glob('aclImdb/test/pos/*')
|
962 |
+
# neg_dev_files = glob.glob('aclImdb/test/neg/*')
|
963 |
+
# num_dev_files_per_class = 100
|
964 |
+
# all_dev_files = pos_dev_files[:num_dev_files_per_class] + neg_dev_files[:num_dev_files_per_class]
|
965 |
+
# # use the same vectorizer from before! otherwise features won't line up
|
966 |
+
# # don't fit it again, just use it to transform!
|
967 |
+
# X_dev = vectorizer.transform(all_dev_files)
|
968 |
+
# y_dev = [1]* num_dev_files_per_class + [0]* num_dev_files_per_class
|
969 |
+
# # don't need new w and b, these are from out existing model
|
970 |
+
# y_dev_pred = predict_y_lr(w,b,X_dev)
|
971 |
+
# print(classification_report(y_dev, y_dev_pred))
|
972 |
+
|
973 |
+
|
974 |
+
def avg_embeddings(doc, model, vocab: set):
|
975 |
+
words = []
|
976 |
+
# remove out-of-vocabulary words
|
977 |
+
with open(doc, "r") as file:
|
978 |
+
for line in file:
|
979 |
+
for word in line.split():
|
980 |
+
words.append(word)
|
981 |
+
vocab.add(word)
|
982 |
+
words = [word for word in words if word in model.wv.index_to_key]
|
983 |
+
if len(words) >= 1:
|
984 |
+
return np.mean(model.wv[words], axis=0)
|
985 |
+
else:
|
986 |
+
return []
|
987 |
+
|
988 |
+
|
989 |
+
|
990 |
+
def sent_vec(sent, cbow):
|
991 |
+
vector_size = cbow.wv.vector_size
|
992 |
+
wv_res = np.zeros(vector_size)
|
993 |
+
# print(wv_res)
|
994 |
+
ctr = 1
|
995 |
+
for w in sent:
|
996 |
+
if w in cbow.wv:
|
997 |
+
ctr += 1
|
998 |
+
wv_res += cbow.wv[w]
|
999 |
+
wv_res = wv_res/ctr
|
1000 |
+
return wv_res
|
1001 |
+
|
1002 |
+
|
1003 |
+
def spacy_tokenizer(sentence):
|
1004 |
+
# Creating our token object, which is used to create documents with linguistic annotations.
|
1005 |
+
# doc = nlp(sentence)
|
1006 |
+
|
1007 |
+
|
1008 |
+
|
1009 |
+
# print(doc)
|
1010 |
+
# print(type(doc))
|
1011 |
+
|
1012 |
+
# Lemmatizing each token and converting each token into lowercase
|
1013 |
+
# mytokens = [ word.lemma_.lower().strip() for word in doc ]
|
1014 |
+
|
1015 |
+
# print(mytokens)
|
1016 |
+
|
1017 |
+
# Removing stop words
|
1018 |
+
# mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]
|
1019 |
+
|
1020 |
+
# return preprocessed list of tokens
|
1021 |
+
return 0
|
1022 |
+
|
1023 |
+
|
1024 |
+
def cbow_classifier(cbow, data, num_sentances):
|
1025 |
+
vocab_len = len(cbow.wv.index_to_key)
|
1026 |
+
|
1027 |
+
embeddings = []
|
1028 |
+
embedding_dict = {}
|
1029 |
+
vocab = set(cbow.wv.index_to_key)
|
1030 |
+
|
1031 |
+
# print("Data len", len(data))
|
1032 |
+
# print("Data at 0", data[0])
|
1033 |
+
|
1034 |
+
X_temp = np.empty([len(data), 1])
|
1035 |
+
X_train_vect = np.array([np.array([cbow.wv[i] for i in ls if i in vocab])
|
1036 |
+
for ls in data])
|
1037 |
+
X_test_vect = np.array([np.array([cbow.wv[i] for i in ls if i in vocab])
|
1038 |
+
for ls in data])
|
1039 |
+
|
1040 |
+
# words = [word for word in words if word in cbow.wv.index_to_key]
|
1041 |
+
for word in vocab:
|
1042 |
+
# embedding[word] = cbow.wv[word]
|
1043 |
+
embeddings.append(np.mean(cbow.wv[word], axis=0))
|
1044 |
+
embedding_dict[word] = np.mean(cbow.wv[word], axis=0)
|
1045 |
+
|
1046 |
+
X = embeddings
|
1047 |
+
|
1048 |
+
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=y)
|
1049 |
+
|
1050 |
+
# print(embeddings)
|
1051 |
+
# print(vocab_len)
|
1052 |
+
|
1053 |
+
# X_train_vect_avg = []
|
1054 |
+
# for v in X_train_vect:
|
1055 |
+
# if v.size:
|
1056 |
+
# X_train_vect_avg.append(v.mean(axis=0))
|
1057 |
+
# else:
|
1058 |
+
# X_train_vect_avg.append(np.zeros(100, dtype=float))
|
1059 |
+
|
1060 |
+
# X_test_vect_avg = []
|
1061 |
+
# for v in X_test_vect:
|
1062 |
+
# if v.size:
|
1063 |
+
# X_test_vect_avg.append(v.mean(axis=0))
|
1064 |
+
# else:
|
1065 |
+
# X_test_vect_avg.append(np.zeros(100, dtype=float))
|
1066 |
+
|
1067 |
+
# # for i, v in enumerate(X_train_vect_avg):
|
1068 |
+
# # print(len(data.iloc[i]), len(v))
|
1069 |
+
|
1070 |
+
# x_0 = X_train_vect_avg[0]
|
1071 |
+
# num_files_per_class = 100
|
1072 |
+
# y = [1] * num_files_per_class + [0] * num_files_per_class
|
1073 |
+
# w = np.zeros(X_train_vect_avg.shape[1])
|
1074 |
+
# x_0_dense = x_0.todense()
|
1075 |
+
# x_0.dot(w)
|
1076 |
+
|
1077 |
+
# w,b = sgd_for_lr_with_ce(X_train_vect_avg, y)
|
1078 |
+
# w
|
1079 |
+
|
1080 |
+
# sorted_vocab = sorted([(k,v) for k,v in enumerate(embedding_dict)],key=lambda x:x[1])
|
1081 |
+
# sorted_vocab = [a for (a,b) in sorted_vocab]
|
1082 |
+
|
1083 |
+
# sorted_words_weights = sorted([x for x in zip(sorted_vocab, w)], key=lambda x:x[1])
|
1084 |
+
# sorted_words_weights[-50:]
|
1085 |
+
|
1086 |
+
# preds = predict_y_lr(w,b,X_train_vect_avg)
|
1087 |
+
|
1088 |
+
# preds
|
1089 |
+
|
1090 |
+
# w,b = sgd_for_lr_with_ce(X_train_vect_avg, y, num_passes=10)
|
1091 |
+
# y_pred = predict_y_lr(w,b,X_train_vect_avg)
|
1092 |
+
# print(classification_report(y, y_pred))
|
1093 |
+
|
1094 |
+
# # compute for dev set
|
1095 |
+
# pos_dev_files = glob.glob('aclImdb/test/pos/*')
|
1096 |
+
# neg_dev_files = glob.glob('aclImdb/test/neg/*')
|
1097 |
+
# num_dev_files_per_class = 100
|
1098 |
+
# all_dev_files = pos_dev_files[:num_dev_files_per_class] + neg_dev_files[:num_dev_files_per_class]
|
1099 |
+
# # use the same vectorizer from before! otherwise features won't line up
|
1100 |
+
# # don't fit it again, just use it to transform!
|
1101 |
+
# # X_dev = vectorizer.transform(all_dev_files)
|
1102 |
+
# # y_dev = [1]* num_dev_files_per_class + [0]* num_dev_files_per_class
|
1103 |
+
# # # don't need new w and b, these are from out existing model
|
1104 |
+
# # y_dev_pred = predict_y_lr(w,b,X_dev)
|
1105 |
+
# # print(classification_report(y_dev, y_dev_pred))
|
1106 |
+
|
1107 |
+
|
1108 |
+
def sgd_for_lr_with_ce(X, y, num_passes=5, learning_rate = 0.1):
|
1109 |
+
|
1110 |
+
num_data_points = X.shape[0]
|
1111 |
+
|
1112 |
+
# Initialize theta -> 0
|
1113 |
+
num_features = X.shape[1]
|
1114 |
+
w = np.zeros(num_features)
|
1115 |
+
b = 0.0
|
1116 |
+
|
1117 |
+
# repeat until done
|
1118 |
+
# how to define "done"? let's just make it num passes for now
|
1119 |
+
# we can also do norm of gradient and when it is < epsilon (something tiny)
|
1120 |
+
# we stop
|
1121 |
+
|
1122 |
+
for current_pass in range(num_passes):
|
1123 |
+
|
1124 |
+
# iterate through entire dataset in random order
|
1125 |
+
order = list(range(num_data_points))
|
1126 |
+
random.shuffle(order)
|
1127 |
+
for i in order:
|
1128 |
+
|
1129 |
+
# compute y-hat for this value of i given y_i and x_i
|
1130 |
+
x_i = X[i]
|
1131 |
+
y_i = y[i]
|
1132 |
+
|
1133 |
+
# need to compute based on w and b
|
1134 |
+
# sigmoid(w dot x + b)
|
1135 |
+
z = x_i.dot(w) + b
|
1136 |
+
y_hat_i = expit(z)
|
1137 |
+
|
1138 |
+
# for each w (and b), modify by -lr * (y_hat_i - y_i) * x_i
|
1139 |
+
w = w - learning_rate * (y_hat_i - y_i) * x_i
|
1140 |
+
b = b - learning_rate * (y_hat_i - y_i)
|
1141 |
+
|
1142 |
+
# return theta
|
1143 |
+
return w,b
|
1144 |
+
|
1145 |
+
|
1146 |
+
def predict_y_lr(w,b,X,threshold=0.5):
|
1147 |
+
|
1148 |
+
# use our matrix operation version of the logistic regression model
|
1149 |
+
# X dot w + b
|
1150 |
+
# need to make w a column vector so the dimensions line up correctly
|
1151 |
+
y_hat = X.dot( w.reshape((-1,1)) ) + b
|
1152 |
+
|
1153 |
+
# then just check if it's > threshold
|
1154 |
+
preds = np.where(y_hat > threshold,1,0)
|
1155 |
+
|
1156 |
+
return preds
|
1157 |
+
|
1158 |
+
|
1159 |
+
def main():
|
1160 |
+
parser = argparse.ArgumentParser(
|
1161 |
+
prog='word_embedding',
|
1162 |
+
description='This program will train a word embedding model using simple wikipedia.',
|
1163 |
+
epilog='To skip training the model and to used the saved model "word2vec.model", use the command --skip or -s.'
|
1164 |
+
)
|
1165 |
+
parser.add_argument('-s', '--skip', action='store_true')
|
1166 |
+
parser.add_argument('-e', '--extra', action='store_true')
|
1167 |
+
parser.add_argument('-b', '--bias', action='store_true')
|
1168 |
+
parser.add_argument('-c', '--compare', action='store_true')
|
1169 |
+
parser.add_argument('-t', '--text', action='store_true')
|
1170 |
+
|
1171 |
+
args = parser.parse_args()
|
1172 |
+
skip_model = None
|
1173 |
+
cbow_model = None
|
1174 |
+
ud_model = None
|
1175 |
+
wiki_model = None
|
1176 |
+
if args.compare:
|
1177 |
+
if args.skip:
|
1178 |
+
# print("Skipping")
|
1179 |
+
cbow_model = Word2Vec.load("word2vec.model")
|
1180 |
+
skip_model = Word2Vec.load("skip2vec.model")
|
1181 |
+
ud_model = KeyedVectors.load("urban2vec.model")
|
1182 |
+
wiki_model = KeyedVectors.load("wiki2vec.model")
|
1183 |
+
elif args.extra:
|
1184 |
+
# print("Extra mode")
|
1185 |
+
cbow_model = Word2Vec.load("word2vec.model")
|
1186 |
+
skip_model = Word2Vec.load("skip2vec.model")
|
1187 |
+
wiki_model = KeyedVectors.load_word2vec_format("wiki-news-300d-1M-subwords.vec", binary=False)
|
1188 |
+
ud_model = KeyedVectors.load_word2vec_format("ud_basic.vec", binary=False)
|
1189 |
+
wiki_model.save("wiki2vec.model")
|
1190 |
+
ud_model.save("urban2vec.model")
|
1191 |
+
else:
|
1192 |
+
cbow_model, skip_model = train_embeddings()
|
1193 |
+
wiki_model = KeyedVectors.load_word2vec_format("wiki-news-300d-1M-subwords.vec", binary=False)
|
1194 |
+
ud_model = KeyedVectors.load_word2vec_format("ud_basic.vec", binary=False)
|
1195 |
+
wiki_model.save("wiki2vec.model")
|
1196 |
+
ud_model.save("urban2vec.model")
|
1197 |
+
compare_embeddings(cbow_model, skip_model, ud_model, wiki_model)
|
1198 |
+
if args.bias:
|
1199 |
+
if args.skip:
|
1200 |
+
# print("Skipping")
|
1201 |
+
cbow_model = Word2Vec.load("word2vec.model")
|
1202 |
+
skip_model = Word2Vec.load("skip2vec.model")
|
1203 |
+
ud_model = KeyedVectors.load("urban2vec.model")
|
1204 |
+
wiki_model = KeyedVectors.load("wiki2vec.model")
|
1205 |
+
elif args.extra:
|
1206 |
+
# print("Extra mode")
|
1207 |
+
cbow_model = Word2Vec.load("word2vec.model")
|
1208 |
+
skip_model = Word2Vec.load("skip2vec.model")
|
1209 |
+
wiki_model = KeyedVectors.load_word2vec_format("wiki-news-300d-1M-subwords.vec", binary=False)
|
1210 |
+
ud_model = KeyedVectors.load_word2vec_format("ud_basic.vec", binary=False)
|
1211 |
+
wiki_model.save("wiki2vec.model")
|
1212 |
+
ud_model.save("urban2vec.model")
|
1213 |
+
else:
|
1214 |
+
cbow_model, skip_model = train_embeddings()
|
1215 |
+
wiki_model = KeyedVectors.load_word2vec_format("wiki-news-300d-1M-subwords.vec", binary=False)
|
1216 |
+
ud_model = KeyedVectors.load_word2vec_format("ud_basic.vec", binary=False)
|
1217 |
+
wiki_model.save("wiki2vec.model")
|
1218 |
+
ud_model.save("urban2vec.model")
|
1219 |
+
quantify_bias(cbow_model, skip_model, ud_model, wiki_model)
|
1220 |
+
if args.text:
|
1221 |
+
if args.skip:
|
1222 |
+
# print("Skipping")
|
1223 |
+
cbow_model = Word2Vec.load("word2vec.model")
|
1224 |
+
else:
|
1225 |
+
cbow_model, skip_model = train_embeddings()
|
1226 |
+
|
1227 |
+
text_classifier(cbow_model)
|
1228 |
+
# data, sents = get_data()
|
1229 |
+
# cbow_classifier(cbow_model, data, sents)
|
1230 |
+
|
1231 |
+
# print("No errors?")
|
1232 |
+
|
1233 |
+
|
1234 |
+
if __name__ == "__main__":
|
1235 |
+
>>>>>>> 7d5b505 (New in-context model with working UI System)
|
1236 |
main()
|