Confusion about num_labels and problem_type in classification logic 🐛
Author: sumanttyagi
Pull request: no
Labels: name: bug

### System Info

Description:

While reviewing the logic for determining function_to_apply in a classification pipeline, I came across the following condition:
`if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
    function_to_apply = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
    function_to_apply = ClassificationFunction.SOFTMAX
`

https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/image_classification.py 

This logic seems to imply that:

SIGMOID is used either when the problem type is "multi_label_classification" or when num_labels == 1

SOFTMAX is used either when the problem type is "single_label_classification" or when num_labels > 1

This raises a few questions:

**shouldn't it be opposite ??**





This raises a few questions:

Should num_labels > 1 be a requirement for multi-label classification?

Should num_labels == 1 always imply single-label classification?

Could there be edge cases where a multi-label classifier has num_labels == 1?

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Load a Hugging Face model with config.num_labels = 1 and config.problem_type = None or unset.

Pass this model into the classification code where function_to_apply is determined using the logic above.

Observe which activation function gets applied (SIGMOID).

Repeat the above with num_labels = 2, problem_type = None, and see that SOFTMAX is used—even if the intention is to treat the problem as multi-label.

### Expected behavior

If problem_type is unset, fallback logic based on num_labels should clearly distinguish single-label from multi-label cases (e.g., multi-label should only apply if num_labels > 1).

for single_label == 1 and for multi label > 1

Comments:
Hi @sumanttyagi, for single-label classification, `num_labels` is the number of possible label categories. In other words, if you were classifying inputs into 4 categories, `[0, 1, 2, 3]` then `num_labels == 4` but this is still single-label classification, because each input only has one label. You apply a softmax in this case because the possibilities are mutually exclusive, and you want their probabilities to sum to 1.

Multi-label classification is less common, and is used when each input can have multiple labels. An example would be a 'tagging' system for movies, like `romance` or `comedy` or `female lead` or `language: english` where each input can have multiple tags, and you want to predict each tag independently. In these cases, each neuron would predict a tag and you would apply a sigmoid to them independently. They are not mutually exclusive and do not need to sum to 1.

@Rocketknight1 
my question is about the below condition 

_if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1_ 
in this above condition 

- [ ] 1.why we have self.model.config.num_labels == 1 ?

- [ ]  2.how does this help ?to identify the problem_type? because many model https://huggingface.co/timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k/blob/main/config.json may models do not have the problem_type they could be multi_label also the above condition would not hold true ?
- [ ] 3. Do we have this expectation or is this documented  that all the mulit_label_classification should have the problem_type defined ?

let's say if config file problem_type is not declared ,similar to your example multi label class like romance or comedy or female lead or language: english then the above condition will not satisfy 


Hi @sumanttyagi, I believe `self.model.config.num_labels == 1` would indicate that the task is actually **binary** classification. In this case, rather than softmax with 2 neurons, you can use sigmoid with 1 neuron.

Hi @Rocketknight1 , I'm also somewhat confused with the multi-label classification term here.
Say I have a movie tagging system, it has 

language: eng or jpn or ...
category: action or comedy or ... 

This looks more like a "multi-head classification" for me, e.g., 2 fc heads (one for language and one for category and then cross-entorpy), how can transformers' multi_label_classification fit in here? Any why are we using sigmoid loss here?

Hi @rxqy, you're correct, that is a problem that requires a network with multiple output heads, each with softmax loss. Can you ask this question in the [support space](https://huggingface.co/spaces/transformers-community/support) instead? One of the engineers there should be able to help with setting up a network for that problem. It probably won't work with the standard `ForSequenceClassification` networks because they only have one output head.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.