Previous Experiments

• exp-001: train gpt-2's tokenizer and finetune gpt-2's embedding layers wte and wpe on HF's OSCAR unshuffled_deduplicated_fr and unshuffled_dudplicated_kr.
• exp-002: evaluate gpt-2 on FLUE's tasks (CLS, XNLI, PAWS)
• exp-003: TODO: evaluate on multiatis
• exp-004: Does the embedding layer learn anything useful? Take a dataset in English for PAWS-X, finetune GPT-2 on this dataset, evaluate it on English test set T_e. Then, take the same test-set T_e translated in French (T_f), take GPT-2 parameters fine-tuned for the task X, replace English embeddings with French embeddings and evaluate thus obtained model on French test set.

Experiment folders below after Conversation with Vassilina, Hady, Iz, and Maruf Link

• exp-005: cleaned from exp-001 for finetuning GPT-2 embedding layers for DE and KO on Oscar.
• exp-006: run zero-shot and finetuned evaluation setting for XNLI ✅, PAWS ❌, and XQuAD ❌. (❌ means not done. ✅ means done.)
• exp-007: apply MAD-X adapter method. Paper link
• exp-008: from exp-006, but using mBERT on the zero-shot and finetuning setting.

Carbon Tracking

Do not forget to log your experiments in this spreadsheet