[_tokenizer] data: {"input": "['\u9ad8\u5c42\u5efa\u7b51', '\u9632\u706b\u5899']", "input_id": [22, 23], "model_type": "user_word"} 编码格式问题
时间: 2024-06-02 10:04:22 浏览: 11
根据您提供的信息,[_tokenizer]是一个编码器,用于将输入文本转换成模型可以处理的编码格式。具体来说,该编码器是基于用户自定义词典的,可以对中文文本进行分词并编码。其中,data中的input表示输入的文本列表,input_id表示输入文本的ID号,model_type表示使用的模型类型。由于该编码器是基于用户自定义词典的,因此可以根据具体需求添加、删除或修改词典中的词语。如果您还有其他问题,请继续提问。
相关问题
{ "index.max_ngram_diff": 10, "analysis": { "analyzer": { "my_ngram_analyzer": { "tokenizer": "my_ngram_tokenizer" } }, "tokenizer": { "my_ngram_tokenizer": { "type": "ngram", "min_gram": 1, "max_gram": 10, "token_chars": [ "letter", "digit" ] } } } }
您提供的是Elasticsearch的索引设置和分析器配置示例。该配置使用了ngram分词器创建了一个名为"my_ngram_analyzer"的自定义分析器,并使用了"my_ngram_tokenizer"作为其分词器。
这个分词器将文本按照ngram的方式进行切分,生成包含不同长度的ngram片段。在这个配置中,min_gram参数设置为1,表示最小的ngram片段长度为1个字符;max_gram参数设置为10,表示最大的ngram片段长度为10个字符。
此外,token_chars参数指定了被切分的字符类型,其中"letter"表示字母字符,"digit"表示数字字符。
这个配置可以用于创建一个支持按照ngram方式进行文本搜索和匹配的索引。您可以将此配置应用到您的索引中,以便进行阿拉伯数字的查询或其他需要ngram方式进行文本匹配的场景。
X_train = df.loc[:25000, 'review'].values y_train = df.loc[:25000, 'sentiment'].values X_test = df.loc[25000:, 'review'].values y_test = df.loc[25000:, 'sentiment'].values from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import GridSearchCV tfidf = TfidfVectorizer(strip_accents=None, lowercase=False, preprocessor=None) param_grid = [{'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, {'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'vect__use_idf':[False], 'vect__norm':[None], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, ] lr_tfidf = Pipeline([('vect', tfidf), ('clf', ******)]) # find out how to use pipeline and choose a model to make the document classification gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid, scoring='accuracy', cv=5, verbose=2, n_jobs=-1) *号部分填什么
You can choose a classifier to use in the pipeline depending on your specific task and the nature of your data. Some commonly used classifiers for document classification include logistic regression, support vector machines (SVM), and naive Bayes.
For example, if you want to use logistic regression as your classifier, you can replace the asterisks with `LogisticRegression(random_state=0)`. The `random_state` parameter ensures that the results are reproducible.
The complete code would look like this:
```
from sklearn.linear_model import LogisticRegression
X_train = df.loc[:25000, 'review'].values
y_train = df.loc[:25000, 'sentiment'].values
X_test = df.loc[25000:, 'review'].values
y_test = df.loc[25000:, 'sentiment'].values
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import GridSearchCV
tfidf = TfidfVectorizer(strip_accents=None,
lowercase=False,
preprocessor=None)
param_grid = [{'vect__ngram_range': [(1, 1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
{'vect__ngram_range': [(1, 1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'vect__use_idf':[False],
'vect__norm':[None],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
]
lr_tfidf = Pipeline([('vect', tfidf),
('clf', LogisticRegression(random_state=0))])
gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid,
scoring='accuracy',
cv=5,
verbose=2,
n_jobs=-1)
```
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