Summary
The issue at hand is low accuracy in training a SetFit model using various embedding models and logistic regression. Despite attempts to adjust parameters such as epochs, iterations, and learning rate, the model consistently outputs the same label for all inputs, suggesting overfitting or collapsing of the embedding model.
Root Cause
The root cause of this issue is likely due to:
- Imbalanced dataset: with some labels having hundreds of examples and others having single digits
- Extreme embeddings: with no in-between values, causing the model to collapse
- Overfitting: the model is too complex and is fitting the noise in the training data rather than the underlying patterns
Why This Happens in Real Systems
This issue can occur in real systems due to:
- Poor data quality: imbalanced or noisy data can cause models to overfit or collapse
- Inadequate model selection: choosing a model that is too complex or not suitable for the task at hand
- Insufficient hyperparameter tuning: failing to adjust parameters such as learning rate, epochs, and iterations can lead to suboptimal performance
Real-World Impact
The impact of this issue can be significant, including:
- Poor model performance: low accuracy and inability to generalize to new data
- Wasted resources: spending time and computational resources on training a model that is not effective
- Lack of trust: in the model and its predictions, leading to decreased adoption and usage
Example or Code
import torch
import torch.nn as nn
import torch.optim as optim
# Define a simple neural network model
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(128, 128) # input layer (128) -> hidden layer (128)
self.fc2 = nn.Linear(128, 8) # hidden layer (128) -> output layer (8)
def forward(self, x):
x = torch.relu(self.fc1(x)) # activation function for hidden layer
x = self.fc2(x)
return x
# Initialize the model, loss function, and optimizer
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)How Senior Engineers Fix It
Senior engineers can fix this issue by:
- Data preprocessing: balancing the dataset and normalizing the embeddings
- Model selection: choosing a more suitable model for the task at hand, such as a transfer learning approach
- Hyperparameter tuning: adjusting parameters such as learning rate, epochs, and iterations to prevent overfitting
- Regularization techniques: applying techniques such as dropout or L1/L2 regularization to prevent overfitting
Why Juniors Miss It
Juniors may miss this issue due to:
- Lack of experience: with machine learning models and datasets
- Insufficient knowledge: of hyperparameter tuning and regularization techniques
- Overreliance on default parameters: failing to adjust parameters such as learning rate, epochs, and iterations
- Inadequate testing: not thoroughly testing the model on a variety of inputs and datasets