Add Language, Gender, and Age classifcation tasks based on common-language Dataset

[anime-sh]

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.

{
  "dataset_revision": "16ea653dd7d6a92f8fd80839466b1c6be1df300a",
  "task_name": "Common-Language-Gender-Detection",
  "mteb_version": "1.36.20",
  "scores": {
    "test": [
      {
        "accuracy": 0.243652,
        "f1": 0.193488,
        "f1_weighted": 0.273044,
        "scores_per_experiment": [
          {
            "accuracy": 0.248535,
            "f1": 0.192381,
            "f1_weighted": 0.286627
          },
          {
            "accuracy": 0.224121,
            "f1": 0.179518,
            "f1_weighted": 0.256078
          },
          {
            "accuracy": 0.242188,
            "f1": 0.184671,
            "f1_weighted": 0.243607
          },
          {
            "accuracy": 0.314453,
            "f1": 0.243716,
            "f1_weighted": 0.366869
          },
          {
            "accuracy": 0.188965,
            "f1": 0.167155,
            "f1_weighted": 0.21204
          }
        ],
        "main_score": 0.243652,
        "hf_subset": "default",
        "languages": [
          "eng-Latn"
        ]
      }
    ]
  },
  "evaluation_time": 457.12063217163086,
  "kg_co2_emissions": null
}

{
  "dataset_revision": "16ea653dd7d6a92f8fd80839466b1c6be1df300a",
  "task_name": "Common-Language-Age-Detection",
  "mteb_version": "1.36.20",
  "scores": {
    "test": [
      {
        "accuracy": 0.080078,
        "f1": 0.054588,
        "f1_weighted": 0.095012,
        "scores_per_experiment": [
          {
            "accuracy": 0.080078,
            "f1": 0.054588,
            "f1_weighted": 0.095012
          }
        ],
        "main_score": 0.080078,
        "hf_subset": "default",
        "languages": [
          "eng-Latn"
        ]
      }
    ]
  },
  "evaluation_time": 84.81777930259705,
  "kg_co2_emissions": null
}

{
  "dataset_revision": "16ea653dd7d6a92f8fd80839466b1c6be1df300a",
  "task_name": "Common-Language-Language-Detection",
  "mteb_version": "1.36.20",
  "scores": {
    "test": [
      {
        "accuracy": 0.04834,
        "f1": 0.032008,
        "f1_weighted": 0.033977,
        "scores_per_experiment": [
          {
            "accuracy": 0.04834,
            "f1": 0.032008,
            "f1_weighted": 0.033977
          }
        ],
        "main_score": 0.04834,
        "hf_subset": "default",
        "languages": [
          "eng-Latn"
        ]
      }
    ]
  },
  "evaluation_time": 119.97620368003845,
  "kg_co2_emissions": null
}

[isaac-chung]

Nice! Just a small suggestion.