Fine-tuning and adaptation

Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

Solving math word problems

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.

Fine tuning CLIP with Remote Sensing (Satellite) images and captions

Improving language model behavior by training on a curated dataset

Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.

Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers

Fine-tuning GPT-2 from human preferences

We’ve fine-tuned the 774M parameter GPT-2 language model using human feedback for various tasks, successfully matching the preferences of the external human labelers, though those preferences did not always match our own. Specifically, for summarization tasks the labelers preferred sentences copied wholesale from the input (we’d only asked them to ensure accuracy), so our models learned to copy.

Retro Contest

We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.