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Found 9 papers, 6 papers with code
AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML.
Datasets: A Community Library for Natural Language Processing
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks.
Transformers: State-of-the-Art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
Ranked #13 on
Semantic Textual Similarity
on STS Benchmark
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
We introduce a new approach to generative data-driven dialogue systems (e. g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model.
Ranked #3 on
Dialogue Generation
on Persona-Chat
Continuous Learning in a Hierarchical Multiscale Neural Network
We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework.
Meta-Learning a Dynamical Language Model
We consider the task of word-level language modeling and study the possibility of combining hidden-states-based short-term representations with medium-term representations encoded in dynamical weights of a language model.
