PyPI 官网下载 | similar-sentences-2.9a9.tar.gz

[](https://badge.fury.io/py/similar-sentences) [](https://www.python.org/downloads/release/python-300/) # Similar sentence Prediction with more accurate results with your dataset on top of BERT pertained model. ## Setup Install the package ```python pip install similar-sentences ``` ### Methods to know #### SimilarSentences(FilePath,Type) * **FilePath**: Reference to model.zip for prediction. Reference to sentences.txt for training.<br/> * **Type**: `predict` or `train` #### .train() * Used for training the setences. Which required `(".txt", "train")` as parameter in SimilarSentences #### .predict(InputSentences, NumberOfPrediction, DesiredJsonOutput) * Used for predicting the setences. Which required `(".zip", "predict")` as parameter in SimilarSentences<br/> * **InputSentences**: To find the similar sentence for. <br/> * **NumberOfPrediction**: Number of results for the prediction<br/> * **DesiredJsonOutput**: The output will be in JSON format. `simple` produces a plain output. `detailed` produces detailed output with score #### .reload() * Used for reloading (or) updating the model. Which required `(".zip", "predict")` as parameter in SimilarSentences ## Getting Started ## Train the model with your dataset Prepare your dataset and save the content to `sentences.txt` ``` Hi, thanks for contacting. Hello there! Hi there, welcome! Hi, how can I help? In a few words, how can help? Hi again, welcome back. Hi! Welcome back. Good morning! Good afternoon! Good evening! Good morning! Welcome. Good afternoon! Welcome. Good evening! Welcome. Hello, how can I help? Welcome. Welcome back. Thanks for contacting. Goodbye! Thanks for contacting. Goodbye! Thanks for contacting. Bye! Happy to help! Glad I could help! ``` Supply the sentences to build the model. ```python from SimilarSentences import SimilarSentences # Make sure the extension is .txt model = SimilarSentences('sentences.txt',"train") model.train() ``` The code snipet will produce model.zip. ## Predicting from your model Load the model.zip from the training. ```python from SimilarSentences import SimilarSentences model = SimilarSentences('model.zip',"predict") text = 'Hi.How are you doing?' simple = model.predict(text, 2, "simple") detailed = model.predict(text, 2, "detailed") print(simple) print(detailed) ``` Output looks like, ```python #simple output [ "Hello there! Did I get that right?", "Right Hi, how can I help?" ] #detailed output [ [ { "sentence": "Hello there!", "score": 0.938870553799856 }, { "sentence": "Did I get that right?", "score": 0.7910412586610753 } ], [ { "sentence": "Right", "score": 0.9161810654762793 }, { "sentence": "Hi, how can I help?", "score": 0.7824734658953297 } ] ] ```` :+1: :sparkles: :camel: :tada: :rocket: :metal: :octocat: HAPPY CODING :octocat: :metal: :rocket: :tada: :camel: :sparkles: :+1: