section2.ipynb

Kernel: Unknown Kernel

Treinando um novo tokenizador

Install the Transformers, Datasets, and Evaluate libraries to run this notebook.

!pip install datasets evaluate transformers[sentencepiece]
!apt install git-lfs

You will need to setup git, adapt your email and name in the following cell.

!git config --global user.email "[email protected]"
!git config --global user.name "Your Name"

You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials.

from huggingface_hub import notebook_login

notebook_login()

from datasets import load_dataset

# Isto pode levar alguns minutos para carregar, então pegue um copo de café enquanto espera!
raw_datasets = load_dataset("code_search_net", "python")

raw_datasets["train"]

Dataset({
    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', 
      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', 
      'func_code_url'
    ],
    num_rows: 412178
})

print(raw_datasets["train"][123456]["whole_func_string"])

# Não remova o comentário da linha abaixo, a menos que o teu dataset seja pequeno!
# training_corpus = [raw_datasets["train"][i: i + 1000]["whole_func_string"] for i in range(0, len(raw_datasets["train"]), 1000)]

training_corpus = (
    raw_datasets["train"][i : i + 1000]["whole_func_string"]
    for i in range(0, len(raw_datasets["train"]), 1000)
)

gen = (i for i in range(10))
print(list(gen))
print(list(gen))

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[]

def get_training_corpus():
    return (
        raw_datasets["train"][i : i + 1000]["whole_func_string"]
        for i in range(0, len(raw_datasets["train"]), 1000)
    )


training_corpus = get_training_corpus()

def get_training_corpus():
    dataset = raw_datasets["train"]
    for start_idx in range(0, len(dataset), 1000):
        samples = dataset[start_idx : start_idx + 1000]
        yield samples["whole_func_string"]

from transformers import AutoTokenizer

old_tokenizer = AutoTokenizer.from_pretrained("gpt2")

example = '''def add_numbers(a, b):
    """Add the two numbers `a` and `b`."""
    return a + b'''

tokens = old_tokenizer.tokenize(example)
tokens

['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ"""', 'Add', 'Ġthe', 'Ġtwo',
 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '."', '""', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']

tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)

tokens = tokenizer.tokenize(example)
tokens

['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ"""', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',
 'a', '`', 'Ġand', 'Ġ`', 'b', '`."""', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']

print(len(tokens))
print(len(old_tokenizer.tokenize(example)))

27
36

example = """class LinearLayer():
    def __init__(self, input_size, output_size):
        self.weight = torch.randn(input_size, output_size)
        self.bias = torch.zeros(output_size)

    def __call__(self, x):
        return x @ self.weights + self.bias
    """
tokenizer.tokenize(example)

['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',
 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',
 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',
 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',
 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']

tokenizer.save_pretrained("code-search-net-tokenizer")

from huggingface_hub import notebook_login

notebook_login()

tokenizer.push_to_hub("code-search-net-tokenizer")

# Substitua "huggingface-course" abaixo pelo seu namespace real para usar seu próprio tokenizador
tokenizer = AutoTokenizer.from_pretrained("huggingface-course/code-search-net-tokenizer")