Updated README with usage instructions and add streamlit as dependency

README.md

@@ -1 +1,27 @@
 # bert-sentiment-analysis
+
+Prototype that classifies text into positive or negative sentiments using a fine tuned bert model
+
+## Installation of dependencies
+
+`pip install -r requirements.txt`
+
+## Usage
+
+1. Download the [trained model](https://drive.google.com/file/d/1yI1yEsAco-U-Ma9uDrJSQV21DnF2n1vU/view?usp=sharing) and move it to the *models* directory
+2. Use the tool:
+* To use it as a **streamlit web app** run:
+
+  `streamlit run sentiment_analysis.py`
+
+  It will open a web app on `http://localhost:8501`
+
+* To use it from **command line** run
+
+  `python sentiment_classificator.py <TEXT_TO_CLASSIFY>`
+
+## Training
+
+1. Download the [all_sentiment_dataset.csv](https://drive.google.com/file/d/175Ccd3B6kLWMBvr1WAUzQJT4TwgzXF6N/view?usp=sharing)
+2. Execute the *classify_sentiment_with_bert* notebook which is in the *notebooks* directory
+3. The model should be saved under *models* directory as **sentiments_bert_model.h5**

requirements.txt

@@ -1,5 +1,6 @@
 pandas
 jupyter
 numpy
+streamlit
 tensorflow
 tensorflow-text