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Author: FernandoLpz

README.md

@@ -3,7 +3,7 @@
 The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a dataset of Tweets provided by Kaggle. 
 
 ## 1. Data
-As it was mentioned above, the dataset we are woking with is about Tweets regarding fake news. The head of the dataset looks like this:
+As it was mentioned above, the implemented dataset is about Tweets regarding fake news. The ``raw``dataset contains some unnecessary columns which are going to be removed in the preprocessing step, in the end, we will be working with a dataset with a head such as this: 
 
 |id| text | target |
 | ------------- | ------------- | ------------- |
@@ -12,7 +12,7 @@ As it was mentioned above, the dataset we are woking with is about Tweets regard
 | 3  | INEC Office in Abia Set Ablaze - http://t.co/3ImaomknnA  | 1 |
 | 4  | Building the perfect tracklist to life leave the streets ablaze  | 0  |
 
-This dataset can be found in ``data/tweets.csv``. 
+This raw dataset can be found in ``data/tweets.csv``. 
 
 ## 2. The model
 As it was already commented, the aim of this repository is to provide a base line model for text classfication. In this sense, the model is based on a two-stacked LSTM layers followed by two linear layers. The dataset is preprocessed through a tokens-based technique, then tokens are associated to an embedding layer. The following image describes the pipeline of the model.
@@ -21,13 +21,33 @@ As it was already commented, the aim of this repository is to provide a base lin
 Working on
 
 ## 4. How to use it
+The model can be executed easily by typing:
 ```
-python -B main.py
+python main.py
 ```
 You can define some hyperparameters manually, such as:
 ```
  main.py [-h] [--epochs EPOCHS] [--learning_rate LEARNING_RATE]
          [--hidden_dim HIDDEN_DIM] [--lstm_layers LSTM_LAYERS]
          [--batch_size BATCH_SIZE] [--test_size TEST_SIZE]
          [--max_len MAX_LEN] [--max_words MAX_WORDS]
+```
+
+## 5. Demo
+The follwowing configuration was implemented in order to achieve the best results
+```
+python -B main.py --epochs 10 --learning_rate 0.01 --hidden_dim 128 --lstm_layers 2 --batch_size 64
+```
+by getting the following output:
+```
+Epoch: 1, loss: 0.53032, Train accuracy: 0.59376, Test accuracy: 0.63099
+Epoch: 2, loss: 0.43361, Train accuracy: 0.63251, Test accuracy: 0.72948
+Epoch: 3, loss: 0.36803, Train accuracy: 0.76141, Test accuracy: 0.75509
+Epoch: 4, loss: 0.26117, Train accuracy: 0.80821, Test accuracy: 0.77807
+Epoch: 5, loss: 0.19844, Train accuracy: 0.83547, Test accuracy: 0.77741
+Epoch: 6, loss: 0.16377, Train accuracy: 0.86453, Test accuracy: 0.77216
+Epoch: 7, loss: 0.02130, Train accuracy: 0.88391, Test accuracy: 0.75509
+Epoch: 8, loss: 0.00315, Train accuracy: 0.89704, Test accuracy: 0.74787
+Epoch: 9, loss: 0.02075, Train accuracy: 0.91018, Test accuracy: 0.76428
+Epoch: 10, loss: 0.01348, Train accuracy: 0.92808, Test accuracy: 0.75378
 ```