CrossEncoder

A custom cross encoder QA model that gives a diagnosis based on the input of symptoms a user describes in natural language.

Data

This Data was sourced from Kaggle

It contains two columns disease and symptoms, the current dataset has 24 diseases listed with 50 symptoms for each in natural language.

In scripts/s2d CrossEncoder comment the line model = nn.DataParallel(model) if multiple GPUs are unavailable for training.

Install all dependencies using pip install -r requirements.txt

Layer (type:depth-idx)	Param #
DataParallel	--
└─CustomCrossEncoder: 1-1	--
└─Embedding: 2-1	2,568,192
└─LSTM: 2-2	6,299,648
└─MultiheadAttention: 2-3	3,148,800
│ └─NonDynamicallyQuantizableLinear: 3-1	1,049,600
└─Dropout: 2-4	--
└─Linear: 2-5	131,200
└─Linear: 2-6	3,096

Total params: 13,200,536
Trainable params: 13,200,536
Non-trainable params: 0

Added multihead attention to the model improved accuracy by 7% using the same hyperparamters (80.41% to 87.01%)
- Model now has 1.3M parameters (from 522K)
Changed embed_dim to 1024 and hidden_dim to 512, while also changing batch_size to 256.
- Model trained for 350 epochs this time and improved accuracy by another 7% (87.01% to 94.58%)
- Model now has 13.2M parameters (from 1.3M)

NOTE: This project is for demo purposes only. For any symptoms/disease, please refer to a Doctor.