Token Classification
29 papers with code • 11 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Token Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|
MNIST
CoNLL 2003
CoNLL 2002
XTREME
WikiNEuRal
Most implemented papers
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
HReynaud/UVT
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We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.
Entity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans Detection
The baseline approach for this problem using the transformer model is to add a token classification head to the language model and fine-tune the layers with the token labeled dataset.
YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective
In this paper, we describe our system used for SemEval 2021 Task 5: Toxic Spans Detection.
BERT got a Date: Introducing Transformers to Temporal Tagging
satya77/Transformer_Temporal_Tagger
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By supplementing training resources with weakly labeled data from rule-based systems, our model surpasses previous works in temporal tagging and type classification, especially on rare classes.
UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
erikdyan/toxic_span_detection
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SEMEVAL 2021
Toxicity is pervasive in social media and poses a major threat to the health of online communities.
General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
However, there is a big gap between the real input scenario and automatic generated corpus.
Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors
Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care.
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter
lcs2-iiitd/daberta-emnlp-2022
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The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation.
Technical Report: Impact of Position Bias on Language Models in Token Classification
mehdibenamorr/Token-Positional-Bias
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Therefore, we conduct an in-depth evaluation of the impact of position bias on the performance of LMs when fine-tuned on token classification benchmarks.
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.
