[Feature]: Support sigmoid for classification models

[tonyay163]

🚀 The feature, motivation and pitch

Right now, we only support softmax for classification models but sigmoid is equally as prevalent, depending on the usecase. I think the simplest way is just to return the logits and let us handle it ourselves. Alternatively, returning both softmax and sigmoid is probably not too much more computation.

Additional context

See: https://wandb.ai/amanarora/Written-Reports/reports/Understanding-Logits-Sigmoid-Softmax-and-Cross-Entropy-Loss-in-Deep-Learning--Vmlldzo0NDMzNTU3#conclusion

Thus, sigmoid is preferred for binary & multi-label classification problems whereas softmax is preferred for multi-class classification problems where we want the model to “pick a class”.

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[tonyay163]

I think I found a workaround:

model = LLM(
    model="...",
    task="classify",
    override_pooler_config=PoolerConfig(
        pooling_type="LAST",
        normalize=False,
        softmax=False,
    )
)

def sigmoid(x: List[float]) -> List[float]:
    return [1 / (1 + math.exp(-val)) for val in x]

outputs = model.classify("...")
probs = sigmoid(outputs[0].outputs.probs)

[github-actions]

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!

[github-actions]

This issue has been automatically closed due to inactivity. Please feel free to reopen if you feel it is still relevant. Thank you!

[maxdebayser]

Related PRs: #18313 and #18838