Add IEMOCAP dataset

[diffunity]

Code Quality

• [✅] Code Formatted: Format the code using make lint to maintain consistent style.

Documentation

• [✅] Updated Documentation: Add or update documentation to reflect the changes introduced in this PR.

Testing

• [❌] New Tests Added: Write tests to cover new functionality. Validate with make test-with-coverage.
• [✅] Tests Passed: Run tests locally using make test or make test-with-coverage to ensure no existing functionality is broken.

Adding datasets checklist

Reason for dataset addition: ...

• [✅] I have run the following models on the task (adding the results to the pr). These can be run using the mteb -m {model_name} -t {task_name} command.
  • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  • intfloat/multilingual-e5-small
• [✅] I have checked that the performance is neither trivial (both models gain close to perfect scores) nor random (both models gain close to random scores).
• [✅] If the dataset is too big (e.g. >2048 examples), considering using self.stratified_subsampling() under dataset_transform()
• [✅] I have filled out the metadata object in the dataset file (find documentation on it here).
• [✅] Run tests locally to make sure nothing is broken using make test.
• [✅] Run the formatter to format the code using make lint.

Adding a model checklist

• I have filled out the ModelMeta object to the extent possible
• I have ensured that my model can be loaded using
  • mteb.get_model(model_name, revision) and
  • mteb.get_model_meta(model_name, revision)
• I have tested the implementation works on a representative set of tasks.

[diffunity]

IEMOCAP

{'accuracy': np.float64(0.12670483983349115),
 'f1': np.float64(0.09695427294093387),
 'f1_weighted': np.float64(0.14250605663702376),
 'main_score': np.float64(0.12670483983349115),
 'scores_per_experiment': [{'accuracy': 0.15587649402390438,
                            'f1': 0.10668864611462359,
                            'f1_weighted': 0.1671551654769295},
                           {'accuracy': 0.15139442231075698,
                            'f1': 0.11189726160522125,
                            'f1_weighted': 0.14830827247384432},
                           {'accuracy': 0.11553784860557768,
                            'f1': 0.09169546488499664,
                            'f1_weighted': 0.13815163727760404},
                           {'accuracy': 0.09412350597609562,
                            'f1': 0.08616618328485233,
                            'f1_weighted': 0.11858795138338912},
                           {'accuracy': 0.11659192825112108,
                            'f1': 0.08832380881497556,
                            'f1_weighted': 0.14032725657335174}]}

[isaac-chung]

Let's downsample this dataset. Thanks!

[isaac-chung]

If the dataset is too big (e.g. >2048 examples), considering using self.stratified_subsampling() under dataset_transform()

This is checked but I don't see that completed in the PR.

[isaac-chung]

Duplicates #2640