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README.md
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* Customization for your own use case is super easy. Just modify intents.json with possible patterns and responses and re-run the training (see below for more info).
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In [this article](https://medium.com/@mlvictoriamaslova/nlp-based-chatbot-in-pytorch-bonus-flask-and-javascript-deployment-474c4e59ceff) on Medium I explain some NLP concepts that underlies building Chatbots.
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---
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## Installation
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### Create an environment
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Whatever you prefer (e.g. conda or venv)
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```
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mkdir myproject
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$ cd myproject
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$ python3 -m venv venv
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```
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### Activate it
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Mac / Linux:
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```
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. venv/bin/activate
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```
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Windows:
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```
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venv\Scripts\activate
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```
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### Install PyTorch and dependencies
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For Installation of PyTorch see official website.
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You also need nltk:
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```
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pip install nltk
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```
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If you get an error during the first run, you also need to install nltk.tokenize.punkt: Run this once in your terminal:
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```
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$ python
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>>> import nltk
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>>> nltk.download('punkt')
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```
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### Usage
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Run
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```
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python train.py
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```
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This will dump data.pth file. And then run
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```
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python chat.py
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```
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---
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title: Clinical Segnemt
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emoji: 🚑
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colorFrom: purple
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colorTo: yellow
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sdk: streamlit
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sdk_version: 1.17.0
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app_file: app.py
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pinned: false
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license: cc-by-3.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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