Only Connect Walls Dataset Task 1 (Grouping)
10 papers with code • 1 benchmarks • 1 datasets
Split data into groups, taking into account knowledge in the form of constraints on points, groups of points, or clusters.
Benchmarks
These leaderboards are used to track progress in Only Connect Walls Dataset Task 1 (Grouping)
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Only Connect Walls Dataset Task 1 (Grouping) models and implementations
OCW
Most implemented papers
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
•
•
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
huggingface/transformers
•
•
NeurIPS 2019
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.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
MPNet: Masked and Permuted Pre-training for Language Understanding
microsoft/MPNet
•
•
NeurIPS 2020
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem.
Learning Word Vectors for 157 Languages
dzieciou/lemmatizer-pl
•
•
LREC 2018
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance.
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
mswzeus/PLUS
•
•
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
microsoft/unilm
•
•
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
In this paper we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections, and identifying correct open knowledge domain connections in respective groups.
