few-shot-ner
8 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in few-shot-ner
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Most implemented papers
Prompt-Based Metric Learning for Few-Shot NER
achen-qaq/proml
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Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples.
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
hccngu/metnet
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We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type.
From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts
br-ai-ns-institute/zero-shotner
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Supervised named entity recognition (NER) in the biomedical domain depends on large sets of annotated texts with the given named entities.
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain
Recent advancements in language models (LMs) have led to the emergence of powerful models such as Small LMs (e. g., T5) and Large LMs (e. g., GPT-4).
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
dongguanting/msdp-fewshot-ner
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The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
