04_tm_nlp_t51:TM NLP TAREA 5版本1

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  • 2022-04-26 01:21
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塔雷亚5-Setanment Analisys 隆美尔·桑蒂佐-91071 土耳其等地的旅行顾问。 礼节措施可减少2个意见。 可以根据自己的情况从模型分析和情感分析中选择一种。 您可能会在“终结点”上遇到任何新的文档模型。 Ejecución: 请准备以下文件: /src/data/prepare_data.py 通用模型: /src/models/train.py Predecir con el modelo: /src/models/predict_model_tripad.py 端点: /src/api/main.py 项目组织 ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The
04_tm_nlp_t51-master.zip
  • 04_tm_nlp_t51-master
  • notebooks
  • Untitled.ipynb
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  • train.ipynb
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  • tokenize.ipynb
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  • prepare_data.ipynb
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  • bag_of_words.ipynb
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  • models
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  • bigrams.pkl
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  • src
  • models
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  • predict.py
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  • features
  • utils.py
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  • __init__.py
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  • tokenize.py
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  • dictionary.py
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  • data
  • split.py
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  • prepare_data.py
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  • main.py
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内容介绍
Tarea 5 - Setiment Analisys ============================== Rommel Santizo - 91071 --- Esta tarea analiza comentarios de Trip Advisor, que estan etiquetados. La etiqueta se reduce a 2 opciones Positivo o Negativo. Y genera un modelo para hacer un analisis de sentimento en base a los documentos y su clase. Y se generá una "End-Point" para ver la predicción del modelo con un documento nuevo. ___ Ejecución: ------------ 1. Leer y preparar datos: **/src/data/prepare_data.py** 2. Generar el modelo: **/src/models/train.py** 4. Predecir con el modelo: **/src/models/predict_model_tripad.py** 5. End-Point: **/src/api/main.py** ___ --- Project Organization ------------ ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │   ├── external <- Data from third party sources. │   ├── interim <- Intermediate data that has been transformed. │   ├── processed <- The final, canonical data sets for modeling. │   └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │   └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported ├── src <- Source code for use in this project. │   ├── __init__.py <- Makes src a Python module │ │ │   ├── data <- Scripts to download or generate data │   │   └── make_dataset.py │ │ │   ├── features <- Scripts to turn raw data into features for modeling │   │   └── build_features.py │ │ │   ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │   │   ├── predict_model.py │   │   └── train_model.py │ │ │   └── visualization <- Scripts to create exploratory and results oriented visualizations │   └── visualize.py │ └── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io -------- <p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/" rel='nofollow' onclick='return false;'>cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
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