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Found 19 papers, 15 papers with code
Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
Using vision-language models (VLMs) in web development presents a promising strategy to increase efficiency and unblock no-code solutions: by providing a screenshot or a sketch of a UI, a VLM could generate the code to reproduce it, for instance in a language like HTML.
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
What Language Model to Train if You Have One Million GPU Hours?
All our models and code are open-sourced at https://huggingface. co/bigscience .
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
PromptSource is a system for creating, sharing, and using natural language prompts.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Block Pruning For Faster Transformers
Pre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models.
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.
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.
Low-Complexity Probing via Finding Subnetworks
The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations.
Learning from others' mistakes: Avoiding dataset biases without modeling them
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task.
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks.
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.
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications.
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 #2 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(Wasserstein Distance (WD) metric, using extra
training data)
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
(using extra training data)
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.
Ranked #10 on
Relation Extraction
on ACE 2005
(using extra training data)
