exp-001
: train gpt-2's tokenizer and finetune gpt-2's embedding layerswte
andwpe
on HF's OSCARunshuffled_deduplicated_fr
andunshuffled_dudplicated_kr
.exp-002
: evaluate gpt-2 on FLUE's tasks (CLS, XNLI, PAWS)exp-003
: TODO: evaluate on multiatisexp-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 fromexp-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 linkexp-008
: from exp-006, but using mBERT on the zero-shot and finetuning setting.
Do not forget to log your experiments in this spreadsheet