在前面幾章節我們講了很多，說了 tokenizer 是怎麼工作的、tokenizaion 的過程、轉換 input ID、padding、attention masks、多序列處理等等。

但是 Transformers API 其實可以透過很方便的功能，自動處理這些操作，當我們直接將句子傳遞給你的分詞器時，會得到一個準備好傳遞給模型的輸入。

簡單來說，這個 API 能夠自動處理文本的前處理過程，讓你可以更輕鬆地使用預訓練模型進行文本處理，而不需要手動處理分詞、轉換和填充等步驟。這使得使用 Transformer 模型變得更加方便且高效。

根據不同目標進行填充

from transformers import BertTokenizer

checkpoint = "bert-base-cased"
tokenizer = BertTokenizer.from_pretrained(checkpoint)

sequences = [
    "My mood is always so good",
    "Listening to my favorite music makes me feel so happy"
]

1. 填充至最大序列長 (最普遍的填充)

model_inputs = tokenizer(sequences, padding="longest")
print(model_inputs)

{'input_ids': [[101, 1422, 6601, 1110, 1579, 1177, 1363, 102, 0, 0, 0, 0, 0],
               [101, 10817, 1158, 1106, 1139, 5095, 1390, 2228, 1143, 1631, 1177, 2816, 102]],
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}

• 最常見的填充方式

2. 填充製模型的最大長度

model_inputs = tokenizer(sequences, padding="max_length")
print(model_inputs)

{'input_ids': [[101, 1422, 6601, 1110, 1579, 1177, 1363, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
               [101, 10817, 1158, 1106, 1139, 5095, 1390, 2228, 1143, 1631, 1177, 2816, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
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 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
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• 會填充到模型支援的最大長度的操作，對於BERT或DistilBERT等模型，它們通常有一個最大的序列長度限制，例如 512 個標記，所以這個結果每一個長度都是 512

3. 填充至指定的最大長度

model_inputs = tokenizer(sequences, padding="max_length", max_length=16)
print(model_inputs)

{'input_ids': [[101, 1422, 6601, 1110, 1579, 1177, 1363, 102, 0, 0, 0, 0, 0, 0, 0, 0], 
                [101, 10817, 1158, 1106, 1139, 5095, 1390, 2228, 1143, 1631, 1177, 2816, 102, 0, 0, 0]], 
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0], 
                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]}

• 告訴分詞器將這些序列填充到最大長度為 16 的形狀，的為了確保所有序列都具有相同的長度

截斷序列

在需要時截斷超過指定長度的部分。這對於確保輸入序列符合模型的要求很重要，特別是當模型有序列長度限制時

1. 截斷長度超過模型所支援的最大長度

# 第一個字串有806
sequences = ["I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life.I've been waiting for a HuggingFace course my whole life.I've been waiting for a HuggingFace course my whole life.I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life. I've been waiting for a HuggingFace course my whole life.I've been waiting for a HuggingFace course my whole life.I've been waiting for a HuggingFace course my whole life.",
             "So have I!"]

model_inputs = tokenizer(sequences, truncation=True)
print(model_inputs)

{'input_ids': [[101, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 102], 
               [101, 1573, 1138, 146, 106, 102]], 
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
                    [0, 0, 0, 0, 0, 0]], 
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
                    [1, 1, 1, 1, 1, 1]]}

• 長度超過模型所支援的最大長度（例如 BERT 或 DistilBERT 的最大長度為 512 標記），這些超過長度的序列就被截斷

2. 截斷至最大長度

sequences = [
    "My mood is always so good",
    "Listening to my favorite music makes me feel so happy"
]

model_inputs = tokenizer(sequences, max_length=8, truncation=True)
print(model_inputs)

{'input_ids': [[101, 146, 112, 1396, 1151, 2613, 1111, 102], 
               [101, 1573, 1138, 146, 106, 102]], 
 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0], 
                    [0, 0, 0, 0, 0, 0]], 
 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], 
                    [1, 1, 1, 1, 1, 1]]}

• 如果任何序列的長度超過這個指定的最大長度（8標記），則這些超過長度的序列就會被截斷，使其長度為8標記

特殊 tokens

如果觀察分詞器返回的輸入 ID，你會發現它們與之前的略有不同。

sequence = "I've been waiting for a HuggingFace course my whole life."

model_inputs = tokenizer(sequence)
print(model_inputs["input_ids"])

tokens = tokenizer.tokenize(sequence)
ids = tokenizer.convert_tokens_to_ids(tokens)
print(ids)

[101, 146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119, 102]
[146, 112, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119]

• 將句子直接傳遞給分詞器時，tokenizer 將自動添加兩個特殊的 token

我們直接將第一個有特殊 token 的 decode 回去

print(tokenizer.decode(model_inputs["input_ids"]))

[CLS] I've been waiting for a HuggingFace course my whole life. [SEP]

• [CLS]：這表示序列的開始（Classification Token）。
• [SEP]：這表示序列的結束或分隔（Separator Token）。

這些特殊 token 的添加方式可能因模型而異。有些模型可能添加不同的特殊 token，而有些模型可能只在序列開始或結束處添加。然而，tokenizer 了解哪些特殊 token 是預期的，並會為你處理這些細節，以確保輸入序列的正確格式。因此無需手動添加這些特殊 token，分詞器會自動處理它們。

從標記 -> 模型

這個範例中，我們看到如何使用 Transformers 的主要 API 來處理多個文本序列、padding、truncation 以及傳遞到模型中進行預測

import torch
from transformers import BertTokenizer, BertForSequenceClassification

checkpoint = "bert-base-cased"
tokenizer = BertTokenizer.from_pretrained(checkpoint)
model = BertForSequenceClassification.from_pretrained(checkpoint)
sequences = [
    "My mood is always so good",
    "Listening to my favorite music makes me feel so happy"
]

tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
output = model(**tokens)
print(output)

SequenceClassifierOutput(
    loss=None, 
    logits=tensor([[-0.8120, -0.6564],
                   [-0.7161, -0.6265]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None
)

1. 我們首先載入預訓練的 BERT 模型和相對應的 tokenizer。

2. 我們有一個包含兩個句子的序列列表 sequences。

3. 使用 tokenizer tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")處理這些序列：

   • padding=True：表示要對序列進行填充，以確保它們具有相同的長度。
   • truncation=True：表示要對序列進行截斷，以確保它們不超過模型支持的最大長度。
   • return_tensors="pt"：表示返回 PyTorch 張量。

4. 最後，我們將處理後的輸入張量傳遞給預訓練的 BERT 模型進行預測

這樣的做法讓你能夠輕鬆地使用 Transformers 库來處理文本數據，而不需要手動處理分詞、填充和截斷等步驟。模型的輸入資料將會根據你的設置自動進行處理，並且你可以方便地使用預訓練模型來進行文本分類等任務。

目前 transforemrs 的學習就到這邊先暫時結束啦(‾◡◝)

參考資料

• https://huggingface.co/learn/nlp-course/chapter2/6?fw=pt
• https://zhuanlan.zhihu.com/p/448852278