from nltk.tokenize import word_tokenize from nltk.stem import WordNetLemmatizer # 下载NLTK词形还原需要的资源 nltk.download('punkt') nltk.download('wordnet') # 示例文本 text = "Natural language processing with Python is powerful and efficient." # 分词 tokens = word_tokenize(text) print("Toke...
tokens = word_tokenize(text) tokens = [word for word in tokens if word.isalnum()] stop_words = set(stopwords.words('english')) tokens = [word for word in tokens if word.lower() not in stop_words] return tokens df['tokens'] = df['text'].apply(preprocess_text) print(df) 三、创...
我们对每个句子进行分词,也就是将一个句子划分成若干个词(token),转换为一个词的序列。 def tokenize(sentences, token='word'): """Split sentences into word or char tokens""" if token == 'word': return [sentence.split(' ') for sentence in sentences] elif token == 'char': return [list(...
After it's trained, a model can calculate an embedding for text that contains multiple tokens. The model tokenizes the text, then calculates an overall embeddings value based on the learned embeddings of the individual tokens. This technique can be used for semantic document searches or adding ...
import nltk nltk.download('punkt') # 下载punkt分词器模型 text = "Hello, my name is John. I am a student." tokens = nltk.word_tokenize(text) print(tokens) 输出: python 复制代码 ['Hello', ',', 'my', 'name', 'is', 'John', '.', 'I', 'am', 'a', 'student', '.'] ...
tokens = word_tokenize(text) return list(break_up_file(tokens, chunk_size, overlap_size)) def convert_to_detokenized_text(tokenized_text): prompt_text = " ".join(tokenized_text) prompt_text = prompt_text.replace(" 's", "'s") return detokenized_text filename = "/content/drive/MyDriv...
tokenize(str) ## ['this', 'Ġis', 'Ġa', 'Ġtoken', 'ization', 'Ġexample'] encoded = tokenizer.encode_plus(str) ## encoded['input_ids']=[0, 42, 16, 10, 19233, 1938, 1246, 2] decoded = tokenizer.decode(encoded['input_ids']) ## ' this is a tokenization example' An...
Alternatively, if you'd like to tokenize text programmatically, useTiktokenas a fast BPE tokenizer specifically used for OpenAI models. Token Limits Depending on themodelused, requests can use up to 128,000 tokens shared between prompt and completion. Some models, like GPT-4 Turbo, have differen...
print("\nOriginal string:") print(text) result = WordPunctTokenizer().tokenize(text) print("\nSplit all punctuation into separate tokens:") print(result) Sample Output:Original string: Reset your password if you just can't remember your old one. Split all punctuation into separate tokens: ...
These steps outline how a BPE tokenizer tokenizes new text: Pretokenization — Split text into individual words. Byte-encoding — Encode each word into sequences of bytes. Merge — By starting at the top of the merge list and progressing through it, iteratively apply each merge to pairs of...