Text Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

100 leaderboard tables shown for this task, 68 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 100 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

167 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 167 until expanded.

Subtasks archive 2025-07-28

16 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 1,308 papers with code (3,635 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  11 Oct 2018 534 repositories listed Syntology ran 204 of 659 samples · 455 unverified · 149 pointer-only (licence)
  We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
• Semi-supervised Sequence Learning
  4 Nov 2015 161 repositories listed
  In our experiments, we find that long short term memory recurrent networks after being pretrained with the two approaches are more stable and generalize better.
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
  26 Jul 2019 67 repositories listed Syntology ran 22 of 48 samples · 26 unverified · 23 pointer-only (licence)
  Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
• Universal Language Model Fine-tuning for Text Classification
  18 Jan 2018 66 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 3 pointer-only (licence)
  Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.
• Bag of Tricks for Efficient Text Classification
  6 Jul 2016 65 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 2 pointer-only (licence)
  This paper explores a simple and efficient baseline for text classification.
• FastText.zip: Compressing text classification models
  12 Dec 2016 44 repositories listed Syntology ran 3 of 18 samples · 15 unverified · 12 pointer-only (licence)
  We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.
• Character-level Convolutional Networks for Text Classification
  4 Sep 2015 30 repositories listed Syntology ran 4 of 20 samples · 16 unverified · 5 pointer-only (licence)
  This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
• XLNet: Generalized Autoregressive Pretraining for Language Understanding
  19 Jun 2019 27 repositories listed Syntology ran 10 of 24 samples · 14 unverified · 3 pointer-only (licence)
  With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
• Distributed Representations of Sentences and Documents
  16 May 2014 27 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models.
• Revisiting Semi-Supervised Learning with Graph Embeddings
  29 Mar 2016 26 repositories listed Syntology ran 15 of 28 samples · 13 unverified · 4 pointer-only (licence)
  We present a semi-supervised learning framework based on graph embeddings.
• Universal Sentence Encoder
  29 Mar 2018 24 repositories listed Syntology ran 1 of 22 samples · 21 unverified · 1 pointer-only (licence)
  For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
• Very Deep Convolutional Networks for Text Classification
  6 Jun 2016 24 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks.
• RoFormer: Enhanced Transformer with Rotary Position Embedding
  20 Apr 2021 20 repositories listed Syntology ran 14 of 16 samples · 2 unverified
  Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information.
• Unsupervised Data Augmentation for Consistency Training
  29 Apr 2019 20 repositories listed Syntology ran 15 of 52 samples · 37 unverified · 9 pointer-only (licence)
  In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in…
• Parameter-Efficient Transfer Learning for NLP
  2 Feb 2019 17 repositories listed Syntology ran 8 of 22 samples · 14 unverified · 3 pointer-only (licence)
  On GLUE, we attain within 0.
• EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
  31 Jan 2019 16 repositories listed Syntology ran 2 of 13 samples · 11 unverified · 1 pointer-only (licence)
  We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
• How to Fine-Tune BERT for Text Classification?
  14 May 2019 15 repositories listed Syntology ran 6 of 18 samples · 12 unverified · 5 pointer-only (licence)
  Language model pre-training has proven to be useful in learning universal language representations.
• Big Bird: Transformers for Longer Sequences
  28 Jul 2020 14 repositories listed Syntology ran 10 of 15 samples · 5 unverified · 11 pointer-only (licence)
  To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
• Fastformer: Additive Attention Can Be All You Need
  20 Aug 2021 13 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  In this way, Fastformer can achieve effective context modeling with linear complexity.
• FNet: Mixing Tokens with Fourier Transforms
  9 May 2021 12 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing…
• Simple Recurrent Units for Highly Parallelizable Recurrence
  8 Sep 2017 11 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
• A C-LSTM Neural Network for Text Classification
  27 Nov 2015 10 repositories listed
  In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification.
• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  24 Jun 2015 10 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
• Graph Convolutional Networks for Text Classification
  15 Sep 2018 9 repositories listed Syntology ran 6 of 16 samples · 10 unverified · 6 pointer-only (licence)
  We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
• PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
  17 Oct 2017 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  First, the majority of datasets for sequential short-text classification (i.
• FlauBERT: Unsupervised Language Model Pre-training for French
  11 Dec 2019 7 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
• Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
  27 Jul 2019 7 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models.
• Simplifying Graph Convolutional Networks
  19 Feb 2019 7 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
• Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
  14 Apr 2021 6 repositories listed
  The approach significantly enhances the performance and interpretability of TM.
• HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
  18 Dec 2020 6 repositories listed
  We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities.

Syntology lines on 26 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.