lower() for s in corpus] word_corpus = Counter([tuple(data) + ("</w>",) for data in toolz.concat(map(self.basic_tokenizer, corpus))]) vocab = self._count_vocab(word_corpus) ### 逐步合并初始词典中的高频二元组 ### for i in range(max_steps): word_corpus, bi_cnt = self._fi...
GPT token estimation and context size utilities without a full tokenizer ai gpt token tiktoken johannschopplich •0.4.1•3 months ago•0dependents•MITpublished version0.4.1,3 months ago0dependentslicensed under $MIT 3,337 vectra
gpt-tokenizer playground Prompt Token Counter for OpenAI Models 1 token ~= 4 chars in English 1 token ~= ¾ words 100 tokens ~= 75 words 1-2 sentence ~= 30 tokens 1 paragraph ~= 100 tokens 1,500 words ~= 2048 tokens 1,000 个 tokens 约为750个英文单词(约400汉字) RLHF huggingface...
class WordPieceTokenizer(BPETokenizer): def _fit_step(self, word_corpus): ngram = 2 bigram_counter = Counter() unigram_counter = Counter() ### 以步长1,窗口尺寸2,在每个单词上滚动,统计二元组频次 ### for token, count in word_corpus.items(): for c in token: unigram_counter[c] += co...
tokenizer transformers transformers.js gpt4 gpt4o gpt3.5 vicuna chatglm baichuan luke_zhang •3.0.3•4 months ago•30dependents•Apache-2.0published version3.0.3,4 months ago30dependentslicensed under $Apache-2.0 5,248 @nlux/highlighter ...
self.root.title("Token Counter") self.btn = tk.Button(self.root, text="Get Token Count", command=self.count_tokens) self.btn.pack(pady=10, padx=10) self.textbox = tk.Text(self.root, height=2) self.textbox.pack(pady=10, padx=10)# Tokenizer Setupself.token_encoders = {"gpt-...
tokenizer = tokenization_bert.BertTokenizer(vocab_file="GPT2-Chinese/cache/vocab.txt") 1. 2. 3. 4. 创建自己DataSet对象 class MyDataSet(torch.utils.data.Dataset): def __init__(self, examples): self.examples = examples def __len__(self): ...
1.16.2: Tokenizer support for gpt-4o 1.16.2: Updates to Beam 1.16.1: Support for the new OpenAI GPT-4o 2024-05-13 model What's New in 1.16.0 · May 9, 2024 · Crystal Clear Beam core and UX improvements based on user feedback Chat cost estimation 💰 (enable it in Labs / ho...
7.end = time.perf_counter() 8.output_text = " " 9.# Convert IDs to words and make the sentence from it 10.for i in output_ids[0]: 11. output_text += tokenizer.convert_tokens_to_string(tokenizer._convert_id_to_token(i)) ...
Additionally, to ensure that the tokenization captures only meaningful words and excludes common words or punctuation, the prompt will include instructions to use NLTK tools like RegexpTokenizer and stopwords. To enhance the filtering process, our prompt instructs ChatGPT to create a list of 50 sup...