text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair_target: Optional[ Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] ] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] ...
The tokenization method is <tokens> <eos> <language code> for source language documents, and <language code> <tokens> <eos> for target language documents.When you tokenize target text, it incorrectly inserts the language code at the end of the sentence instead of the beginning....
example_input_batch, example_target_batch = next(iter(dataset)) print(example_input_batch.shape, example_target_batch.shape) # 初始化Tokenizer,之前已经对句子做了预处理,这里直接按照空格将每个token切分出来 targ_lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='') # 对目标语言数据集进...
print(example_input_batch.shape, example_target_batch.shape) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. # 初始化Tokenizer,之前已经对句子做了预处理,这里直接按照空格将每个token切分出来 targ_lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='') # 对目标语言数据集进行to...
vocab_files_target = {**cls.resource_files_names, **additional_files_names} # From HF Hub or AI Studio if from_hf_hub or from_aistudio: # Only include the necessary resource files specified by the tokenizer cls Expand All @@ -1541,8 +1558,8 @@ def from_pretrained(cls, pretrained_...
vocab_files_target = {'vocab_file': 'spiece.model', 'added_tokens_file': 'added_tokens.json', 'special_tokens_map_file': 'special_tokens_map.json', 'tokenizer_config_file': 'tokenizer_config.json'} 这里循环的时候先调用else之中的内容 ...
IUITextDropDelegate IUITextDroppable IUITextDropRequest IUITextFieldDelegate IUITextInput IUITextInputDelegate IUITextInputTokenizer IUITextInputTokenizer 方法 IUITextInputTraits IUITextPasteConfigurationSupporting IUITextPasteDelegate IUITextPasteItem IUITextViewDelegate IUITimingCurveProvider IUIToolbarDelegate ...
# 需要导入模块: from keras.preprocessing.text import Tokenizer [as 别名]# 或者: from keras.preprocessing.text.Tokenizer importtexts_to_sequences[as 别名]defpreprocess_embedding():corpus_train, target, filenames = get_corpus() tokenizer = Tokenizer() ...
We continue this process of identifying, scoring, and appending high-scoring pairs until we hit the target number of tokens we wish to include in our vocabulary. So, by setting the desired number of tokens to60, we get the following vocabulary: ...
target_namespace: str, source_tokenizer:Tokenizer= None, target_tokenizer:Tokenizer= None, source_token_indexers: Dict[str, TokenIndexer] = None, target_token_indexers: Dict[str, TokenIndexer] = None, lazy: bool = False, )->None:super().__init__(lazy) ...