tokenize

问题 I am using a pre-trained BERT model to tokenize a text into meaningful tokens. However, the text has many specific words and I don't want BERT model to break them into word-pieces. Is there any solution to it? For example: tokenizer = BertTokenizer('bert-base-uncased-vocab.txt') tokens = tokenizer.tokenize("metastasis") Create tokens like this: ['meta', '##sta', '##sis'] However, I want to keep the whole words as one token, like this: ['metastasis'] 回答1: You are free to add new tokens to the

问题 I have configured path_analyzer in elasticsearch using below configuration. PUT /elastic_course { "settings": { "analysis": { "analyzer": { "path_analyzer": { "tokenizer": "path_tokenizer" }, "reverse_path_analyzer": { "tokenizer": "path_tokenizer" } }, "tokenizer": { "path_tokenizer": { "type": "path_hierarchy", "delimiter": "/", "replacement": "-" }, "reverse_path_tokenizer": { "type": "path_hierarchy", "delimiter": "/", "replacement": "-" } } } }, "mappings": { "book" : { "properties": {

问题 I have a program which allows the user to enter a filter in a textbox in the column header of a DataGridView. This text is then parsed into a list of FilterOperations. Currently i tokenize the string and then build the list in a hunge For-loop. Which Desing Patterns could i use to get rid of the huge for consruct? Are there any other actions i can take to improve the design? In the current state its hard to add support for another operator, datatype or build something else thant the

问题 I'm looking to ignore characters in-between words using NLTK word_tokenize. If I have a a sentence: test = 'Should I trade on the S&P? This works with a phone number 333-445-6635 and email test@testing.com' The word_tokenize method is splitting the S&P into 'S','&','P','?' Is there a way to have this library ignore punctuation between words or letters? Expected output: 'S&P','?' 回答1: Let me know how this works with your sentences. I added an additional test with a bunch of punctuation. The

问题 I'm looking at NLP preprocessing. At some point I want to implement a context-sensitive word embedding, as a way of discerning word sense, and I was thinking about using the output from BERT to do so. I noticed BERT uses WordPiece tokenization (for example, "playing" -> "play" + "##ing"). Right now, I have my text preprocessed using a standard tokenizer that splits on spaces / some punctuation, and then I have a lemmatizer ("playing" ->"play"). I'm wondering what the benefit of WordPiece

问题 So I am trying to encode a string in a tensorflow dataset in order to use it to train a pretrained RoBERTa model. The training_dataset is a tensorflow dataset made from a pandas dataframe that looks like this: I used this dataframe to construct the tf.data.Dataset using: features = ['OptionA', 'OptionB', 'OptionC'] training_dataset = ( tf.data.Dataset.from_tensor_slices( ( tf.cast(train_split[features].values, tf.string), tf.cast(train_split['Answer'].values, tf.int32) ) ) ) Now I want to

问题 So I am trying to encode a string in a tensorflow dataset in order to use it to train a pretrained RoBERTa model. The training_dataset is a tensorflow dataset made from a pandas dataframe that looks like this: I used this dataframe to construct the tf.data.Dataset using: features = ['OptionA', 'OptionB', 'OptionC'] training_dataset = ( tf.data.Dataset.from_tensor_slices( ( tf.cast(train_split[features].values, tf.string), tf.cast(train_split['Answer'].values, tf.int32) ) ) ) Now I want to

问题 So I am trying to encode a string in a tensorflow dataset in order to use it to train a pretrained RoBERTa model. The training_dataset is a tensorflow dataset made from a pandas dataframe that looks like this: I used this dataframe to construct the tf.data.Dataset using: features = ['OptionA', 'OptionB', 'OptionC'] training_dataset = ( tf.data.Dataset.from_tensor_slices( ( tf.cast(train_split[features].values, tf.string), tf.cast(train_split['Answer'].values, tf.int32) ) ) ) Now I want to

来源： https://stackoverflow.com/questions/23509481/tokenizing-first-and-last-name-as-one-token

来源： https://stackoverflow.com/questions/23509481/tokenizing-first-and-last-name-as-one-token