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from datasets import load_dataset
from sympy import Line2D
from transformers import (
AutoTokenizer,
DataCollatorForLanguageModeling,
AutoModelForMaskedLM,
TrainingArguments,
Trainer,
pipeline,
)
import evaluate
import numpy as np
import torch
import math
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
class MaskedLM():
def __init__(self):
self.model = None
self.metric = None
self.data_collator = None
self.raw_data = None
self.model_checkpoint = None
self.tokenized_dataset = None
self.chunk_size = 128
self.chunks_dataset = None
self.split_dataset = None
self.args = None
def load_dataset(self, name="imdb"):
self.raw_data = load_dataset(name)
print("Name of dataset: ", name)
print(self.raw_data)
def load_support(self, mlm_probability=0.15):
self.model_checkpoint = "distilbert-base-uncased"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_checkpoint)
self.data_collator = DataCollatorForLanguageModeling(tokenizer=self.tokenizer, mlm_probability=mlm_probability)
print("Name of model checkpoint: " + self.model_checkpoint)
print("Tokenizer Fast: ", self.tokenizer.is_fast)
print("Symbol of masked word after tokenizer: ", self.tokenizer.mask_token)
print("Model max length tokenizer: ", self.tokenizer.model_max_length)
def explore_infoModel(self, k=5):
model = AutoModelForMaskedLM.from_pretrained(self.model_checkpoint)
model_parameters = model.num_parameters() / 1000000
print(f">>> Number of parameters of {self.model_checkpoint}: {round(model_parameters)}M")
example = "This is a great [MASK]."
print("\n")
print(">>> Example: ", example)
inputs = self.tokenizer(example, return_tensors='pt')
token_logits = model(**inputs).logits
print(f"{'Number of tokens: ':<{30}}{ len(inputs.tokens())}")
print(f"{'Tokens prepare for training: ':<{30}}{ inputs.tokens()}")
print(f"{'IDs of tokens: ':<{30}}{ inputs.input_ids}")
print(f"{'Maked IDs of Model: ':<{30}}{self.tokenizer.mask_token_id}")
print(f"{'Logits of example: ':<{30}}{token_logits}")
print(f"{'Shape of logits: ':<{30}}{token_logits.size()}")
'''Find POSITION of [MASK] => EXTRACT LOGIT'''
mask_token_index = torch.where(inputs.input_ids == self.tokenizer.mask_token_id)[1]
print(f"{'Position of masked token index: ':<{30}}{mask_token_index}")
'''Find LOGIT of token in VOCAB suitable for [MASK]'''
mask_token_logits = token_logits[0, mask_token_index, :]
print(f"{'Logit of tokens in Vocab for [MASK]: ':<{30}}{mask_token_logits}")
'''Choose TOP CANDIDATES for [MASK] with highest logits => TOP LOGITS + POSITION of token suitable for [MASK] in VOCAB'''
top_k_values = torch.topk(mask_token_logits, k, dim=1).values[0].tolist()
print(f"{'Top value of suitable token in Vocab: ':<{30}}{top_k_values }")
top_k_tokens = torch.topk(mask_token_logits, k, dim=1).indices[0].tolist()
print(f"{'Position of suitable token in Vocab: ':<{30}}{top_k_tokens}")
'''Show TOP CANDIDATES'''
for token in top_k_tokens:
print(">>> ", example.replace(self.tokenizer.mask_token, self.tokenizer.decode([token])))
def get_feature_items(self, set="train", index=0, feature="text"):
return None if self.raw_data[set][index][feature] is None or self.raw_data[set][index][feature] == 0 else self.raw_data[set][index][feature]
def get_pair_items(self, set="train", index=0, feature1="text", feature2="label"):
feature1 = self.get_feature_items(set, index, feature1)
feature2 = self.get_feature_items(set, index, feature2)
if feature2 is not None:
line1 = ""
line2 = ""
for word, label in zip(feature1, feature2):
line1 += str(word)
line2 += str(label)
return line1, line2
return feature1, feature1
def get_tokenizer(self, set="train", index=0, feature="text"):
inputs = self.tokenizer(self.get_feature_items(set, index, feature))
return inputs.tokens(), inputs.word_ids()
def tokenizer_dataset(self, example):
inputs = self.tokenizer(example["text"])
inputs["word_ids"] = [inputs.word_ids(i) for i in range(len(inputs["input_ids"]))]
return inputs
def map_tokenize_dataset(self):
print("Start of processing dataset")
self.tokenized_dataset = self.raw_data.map(self.tokenizer_dataset, batched=True, remove_columns=["text","label"] )
print("Done mapping")
print("Tokenized dataset: ", self.tokenized_dataset)
def group_text_chunk(self, example):
'''Group all of text'''
concatenate_example = {k : sum(example[k], []) for k in example.keys()}
'''Compute the length of all'''
total_length = len(concatenate_example["input_ids"])
'''Final length for chunk size'''
total_length = (total_length // self.chunk_size) *self.chunk_size
'''Divide into chunks with chunk size'''
chunks = {
k: [t[i: i + self.chunk_size] for i in range(0, total_length, self.chunk_size)]
for k, t in concatenate_example.items()
}
'''Create LABELS column from INPUT_IDS'''
chunks["labels"] = chunks["input_ids"].copy()
return chunks
def map_chunk_dataset(self):
print("Start of processing dataset")
self.chunks_dataset = self.tokenized_dataset.map(self.group_text_chunk, batched=True)
print("Done mapping")
print("Chunked dataset: ", self.chunks_dataset)
def dataset_split(self, test_size=0.2):
self.split_dataset = self.chunks_dataset["train"].train_test_split(
test_size=test_size, seed=42
)
print("Preparing dataset: ", self.split_dataset)
def create_model(self):
print("Start creating model")
self.model = AutoModelForMaskedLM.from_pretrained(self.model_checkpoint)
print(self.model)
def create_argumentTrainer(self, output_dir="fine_tuned_", eval_strategy="epoch", logging_strategy="epoch",
learning_rate=2e-5, num_train_epochs=20, weight_decay=0.01, batch_size=64,
save_strategy="epoch", push_to_hub=False, hub_model_id="", fp16=True):
logging_steps = len(self.split_dataset["train"]) // batch_size
self.args= TrainingArguments(
#use_cpu=True,
output_dir=f"{output_dir}{self.model_checkpoint}",
overwrite_output_dir=True,
eval_strategy=eval_strategy,
save_strategy=save_strategy,
weight_decay=weight_decay,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
fp16=fp16,
logging_steps=logging_steps
)
print("Arguments ready for training")
return self.args
def call_train(self, model_path="pretrained_model_", set_train="train", set_val="test", push_to_hub=False, save_local=False):
trainer = Trainer(
model=self.model,
args=self.args,
train_dataset=self.split_dataset[set_train],
eval_dataset=self.split_dataset[set_val],
data_collator=self.data_collator,
tokenizer=self.tokenizer,
)
eval_result1 = trainer.evaluate()
print("Perplexity before of training: ", math.exp(eval_result1['eval_loss']))
print("Start training")
trainer.train()
print("Done training")
eval_result2 = trainer.evaluate()
print("Perplexity after of training: ", math.exp(eval_result2['eval_loss']))
if save_local:
trainer.save_model(model_path+self.model_checkpoint)
print("Done saving to local")
if push_to_hub:
trainer.push_to_hub(commit_message="Training complete")
print("Done pushing push to hub")
def call_pipeline(self, local=False, path="", example=""):
if local:
model_checkpoint = ""
else:
model_checkpoint = path
mask_filler = pipeline(
"fill-mask",
model=model_checkpoint,
)
print(mask_filler(example))
if __name__ == "__main__":
'''
1_LOADING DATASET
'''
mlm = MaskedLM()
mlm.load_dataset()
print("-"*50, "Exploring information of Supporting", "-"*50)
mlm.load_support()
print("-"*50, "Exploring information of Supporting", "-"*50)
'''
2_EXPLORING DATASET, MODEL
'''
print("-"*50, "Exploring some information of Model", "-"*50)
mlm.explore_infoModel()
print("-"*50, "Exploring some information of Model", "-"*50)
print("Example[0] (text) in dataset: ", mlm.get_feature_items(set="train", index=0, feature="text")[:100] + "...")
print("Example[0] (label) in dataset: ", mlm.get_feature_items(set="train", index=0, feature="label"))
line1, line2 = mlm.get_pair_items(set="train", index=1, feature1="text", feature2="label")
print("--> Inp of Example[1]: ", line1[:20] + "...")
print("--> Out of Example[1]: ", line2[:20]+ "...")
'''
3_PRE-PROCESSING DATASET, COMPUTE METRICS
'''
tokens, word_ids = mlm.get_tokenizer(set="train", index=0, feature="text")
print("Tokens List of Example 0: ",tokens)
print("Word IDs List of Example 0: ",word_ids)
mlm.map_tokenize_dataset()
mlm.map_chunk_dataset()
mlm.dataset_split()
'''
4_INITIALIZATION MODEL
'''
print("-"*50, f"Information of {mlm.model_checkpoint}", "-"*50)
mlm.create_model()
print("-"*50, f"Information of {mlm.model_checkpoint}", "-"*50)
'''
5_SELECTION HYPERPARMETERS
'''
mlm.create_argumentTrainer(push_to_hub=True, hub_model_id="Chessmen/"+"fine_tune_" + mlm.model_checkpoint)
mlm.call_train(save_local=True,push_to_hub=True)
'''
6_USE PRE-TRAINED MODEL
'''
mlm.call_pipeline(path="Chessmen/fine_tune_distilbert-base-uncased",example= "This is a great [MASK].")
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