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* Initial support for Unitxt datasets in LM Eval Harness

See  https://github.com/IBM/unitxt

The script 'generate_yamls.py' creates LM Eval Harness yaml files corresponding to Unitxt datasets specified in the 'unitxt_datasets' file.

The glue code required to register Unitxt metrics is in 'unitxt_wrapper.py'.

* Added dataset loading check to generate_yaml

Improved error messages.

* Speed up generate_yaml

Added printouts and improved error message

* Added output printout

* Simplified integration of unitxt datasets

Store all the common yaml configuration in a yaml include shared by all datasets of the same task.

* Post code review comments - part 1

1. Made sure include files don't end wth 'yaml' so they won't be marked as tasks
2. Added more datasets and tasks (NER, GEC)
3. Added README

* Post code review comments - part 2

1. Added install unitxt install option in pyproject.toml:
pip install 'lm_eval[unit...

* add Hendrycks MATH (no sympy checking) variant

* add readmes for MATH tasks

* upload new tasks

* add readmes

* run linters

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Co-authored-by: haileyschoelkopf <hailey@eleuther.ai>

* Update utils.py

This is a 4-choice task, option_e is null for all but 3 samples

* Fix options

Adaptive choices

* add option e

* bump multilingual arc version

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Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* Add Pile-10k readme

* Add Pile-10k task configuration file

* Support individual scrolls datasets

* Add qmsum context

* Fix formatting

* Update task.py

* Update __init__.py

* add xnli_eu tasks

* update tasks readme

* update readme

* implementation of TMMLU+

* implemented: TMMLU+

****TMMLU+ : large-scale Traditional chinese Massive Multitask language Understanding****

- 4 categories
    - STEM
    - Social Science
    - Humanities
    - Other

The TMMLU+ dataset, encompassing over 67 subjects and 20160 tasks, is six times larger and more balanced than its predecessor, TMMLU, and includes benchmark results from both closed-source and 20 open-weight Chinese large language models with 1.8B to 72B parameters. However, Traditional Chinese variants continue to underperform compared to major Simplified Chinese models.

```markdown
Total number of tasks in the 'test' sets: 20160
Total number of tasks in the 'validation' sets: 2247
Total number of tasks in the 'train' sets: 335
```

* Remove print from __init__.py

There was my mistake in forgetting to remove the debug print from the code.

* update: move TMMLU+ config generation program into default

* fix: we should use training se...