Unigram tokenization

!pip install datasets evaluate transformers[sentencepiece]

corpus = [
    "This is the Hugging Face Course.",
    "This chapter is about tokenization.",
    "This section shows several tokenizer algorithms.",
    "Hopefully, you will be able to understand how they are trained and generate tokens.",
]

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")

from collections import defaultdict

word_freqs = defaultdict(int)
for text in corpus:
    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)
    new_words = [word for word, offset in words_with_offsets]
    for word in new_words:
        word_freqs[word] += 1

word_freqs

char_freqs = defaultdict(int)
subwords_freqs = defaultdict(int)
for word, freq in word_freqs.items():
    for i in range(len(word)):
        char_freqs[word[i]] += freq
        # Lặp qua các từ con có độ dài >= 2
        for j in range(i + 2, len(word) + 1):
            subwords_freqs[word[i:j]] += freq

# Sắp xếp các từ con theo tần suất
sorted_subwords = sorted(subwords_freqs.items(), key=lambda x: x[1], reverse=True)
sorted_subwords[:10]

token_freqs = list(char_freqs.items()) + sorted_subwords[: 300 - len(char_freqs)]
token_freqs = {token: freq for token, freq in token_freqs}

from math import log

total_sum = sum([freq for token, freq in token_freqs.items()])
model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}

def encode_word(word, model):
    best_segmentations = [{"start": 0, "score": 1}] + [
        {"start": None, "score": None} for _ in range(len(word))
    ]
    for start_idx in range(len(word)):
        # Nó nên được lấp đầy bởi các bước phía trước của vòng lặp
        best_score_at_start = best_segmentations[start_idx]["score"]
        for end_idx in range(start_idx + 1, len(word) + 1):
            token = word[start_idx:end_idx]
            if token in model and best_score_at_start is not None:
                score = model[token] + best_score_at_start
                # Nếu chúng ta tìm thấy một phân đoạn kết thúc tốt hơn tại end_idx, chúng ta cập nhật
                if (
                    best_segmentations[end_idx]["score"] is None
                    or best_segmentations[end_idx]["score"] > score
                ):
                    best_segmentations[end_idx] = {"start": start_idx, "score": score}

    segmentation = best_segmentations[-1]
    if segmentation["score"] is None:
        # Ta đã không tìm thấy tokenize của từ -> không xác định
        return ["<unk>"], None

    score = segmentation["score"]
    start = segmentation["start"]
    end = len(word)
    tokens = []
    while start != 0:
        tokens.insert(0, word[start:end])
        next_start = best_segmentations[start]["start"]
        end = start
        start = next_start
    tokens.insert(0, word[start:end])
    return tokens, score

print(encode_word("Hopefully", model))
print(encode_word("This", model))

def compute_loss(model):
    loss = 0
    for word, freq in word_freqs.items():
        _, word_loss = encode_word(word, model)
        loss += freq * word_loss
    return loss

compute_loss(model)

import copy


def compute_scores(model):
    scores = {}
    model_loss = compute_loss(model)
    for token, score in model.items():
        # Ta luôn giữ độ dài các token bằng 1
        if len(token) == 1:
            continue
        model_without_token = copy.deepcopy(model)
        _ = model_without_token.pop(token)
        scores[token] = compute_loss(model_without_token) - model_loss
    return scores

scores = compute_scores(model)
print(scores["ll"])
print(scores["his"])

percent_to_remove = 0.1
while len(model) > 100:
    scores = compute_scores(model)
    sorted_scores = sorted(scores.items(), key=lambda x: x[1])
    # Loại token percent_to_remove với điểm thấp nhất.
    for i in range(int(len(model) * percent_to_remove)):
        _ = token_freqs.pop(sorted_scores[i][0])
    total_sum = sum([freq for token, freq in token_freqs.items()])
    model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}

def tokenize(text, model):
    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)
    pre_tokenized_text = [word for word, offset in words_with_offsets]
    encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text]
    return sum(encoded_words, [])


tokenize("This is the Hugging Face course.", model)