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Orca-2-13B
2024年1月
Orca 2 is a finetuned version of LLAMA-2. It is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model…
Orca-2-7B
2024年1月
Orca 2 is a finetuned version of LLAMA-2. It is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model…
dp-transformers repository
2022年8月
Motivated by our recent work, we are releasing a repository for training transformer models with differential privacy. Our repository is based on integrating the Opacus library to the Hugging Face platform. We aim to serve the privacy-preserving ML community in utilizing the state-of-the-art models while…
KID: Knowledge Infused Decoding
2022年3月
Knowledge Infused Decoding (KID) is a decoding algorithm that infuses knowledge (from Wikipedia) into each step decoding of text generation.
LiST (Lite Self-Training)
2021年10月
We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST significantly improves over recent methods that adopt prompt fine-tuning using two key techniques. The first one is the use of…
Meta Self-training for Few-shot Neural Sequence Labeling [Code]
2021年10月
This is the implementation of the paper Meta Self-training for Few-shot Neural Sequence Labeling. MetaST is short for meta-learning for self-training.
Baselines for Multilingual Reply Suggestion (MRS)
2021年8月
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning…
Meta Representation Transformation for Low-resource Cross-Lingual Learning [Code]
2021年5月
This is a source code release for a published research at NAACL 2021. Paper Title: MetaXL: Meta Representation Transformation for Low-resource Cross-Lingual Learning Paper Abstract: The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most…
Self-training with Weak Supervision [Code]
2021年4月
State-of-the-art deep neural networks require large-scale labeled training data that is often either expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to…