Todos

### Evaluation
- [x] Code to clean up Dataset/Map for [HF release](https://github.com/bigscience-workshop/ShadesofBias/blob/master/map_dataset.py)
- [x] Add code to constrain generation to just a few tokens e.g. Y/N and retrieve probability, constraint](https://github.com/bigscience-workshop/ShadesofBias/commit/2aa441ef65c411520da5798edd64a34ee10195c6)(Models may be biased to answer Y, so may want free generation [
- [x] Finalize Base Model List (Bloom, llama3, Qwen, mt5, PolyLM)
- [x] Finalize Aligned Model List (BloomZ, llama3-instruct, Qwen-chat,mt0, cohere CMD,Aya Cohere) + (GPT4/4o, Claude Opus, Gemini, )
- [ ] Run biased variations for Base Models + apply Min-Max scaling
- [x] Output the predictions/log probs to prediction files, so that the Inference and Evaluation are separate.
- [x] Aligned Model Evaluation Design (ensure necessary information can be extracted with Endpoint API, also consideration of closed APIs)
- [x] Instruction Template Design
- [x] Metrics Computation for the predicted results (Per-language, aggregate)

- [ ] Conduct any other insightful evaluations for the paper leveraging the full dataset

Start with map_dataset.py and the existing prediction files and evaluation workflow. Review the unchecked base-model biased variations and Min-Max scaling task, then define any additional evaluations supported by the full dataset. Done means the selected evaluations run successfully and produce documented results for the paper.

Written by the indexing model from the issue text.