Alternatives and similar repositories for AttentionExplanation

Users that are interested in AttentionExplanation are comparing it to the libraries listed below

• ShilinHe / interpretableNLP
  A list of publications on NLP interpretability (Welcome PR)
  ☆168Updated 4 years ago
• sarahwie / attention
  Code for EMNLP 2019 paper "Attention is not not Explanation"
  ☆58Updated 3 years ago
• clarkkev / attention-analysis
  ☆463Updated 4 years ago
• Eric-Wallace / interpretability-tutorial-emnlp2020
  Materials for the EMNLP 2020 Tutorial on "Interpreting Predictions of NLP Models"
  ☆199Updated 4 years ago
• serrano-s / attn-tests
  Checking the interpretability of attention on text classification models
  ☆48Updated 5 years ago
• Maluuba / gensen
  Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
  ☆311Updated 4 years ago
• nelson-liu / contextual-repr-analysis
  A toolkit for evaluating the linguistic knowledge and transferability of contextual representations. Code for "Linguistic Knowledge and T…
  ☆209Updated 3 years ago
• allenai / vampire
  Variational Methods for Pretraining in Resource-limited Environments
  ☆174Updated 4 years ago
• tanyuqian / learning-data-manipulation
  NeurIPS 2019 - Learning Data Manipulation for Augmentation and Weighting
  ☆109Updated 4 years ago
• LiyuanLucasLiu / Transformer-Clinic
  Understanding the Difficulty of Training Transformers
  ☆329Updated 2 years ago
• sanyam5 / skip-thoughts
  The first public PyTorch implementation of Skip-Thought Vectors
  ☆224Updated 7 years ago
• john-hewitt / structural-probes
  Codebase for testing whether hidden states of neural networks encode discrete structures.
  ☆391Updated last year
• airsplay / vokenization
  PyTorch code for EMNLP 2020 Paper "Vokenization: Improving Language Understanding with Visual Supervision"
  ☆188Updated 4 years ago
• tommccoy1 / hans
  Heuristic Analysis for NLI Systems
  ☆125Updated 4 years ago
• jayded / eraserbenchmark
  A benchmark for understanding and evaluating rationales: http://www.eraserbenchmark.com/
  ☆96Updated 2 years ago
• jamie-murdoch / ContextualDecomposition
  Demo for method introduced in "Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs"
  ☆56Updated 4 years ago
• jiacheng-xu / vmf_vae_nlp
  Code for EMNLP18 paper "Spherical Latent Spaces for Stable Variational Autoencoders"
  ☆169Updated 6 years ago
• facebookresearch / unlikelihood_training
  Neural Text Generation with Unlikelihood Training
  ☆309Updated 3 years ago
• cbaziotis / neat-vision
  Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Process…
  ☆250Updated 7 years ago
• pmichel31415 / are-16-heads-really-better-than-1
  Code for the paper "Are Sixteen Heads Really Better than One?"
  ☆171Updated 5 years ago
• acmi-lab / counterfactually-augmented-data
  Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
  ☆170Updated 4 years ago
• shaohua0116 / ICLR2019-OpenReviewData
  Script that crawls meta data from ICLR OpenReview webpage. Tutorials on installing and using Selenium and ChromeDriver on Ubuntu.
  ☆387Updated 5 years ago
• graykode / ALBERT-Pytorch
  Pytorch Implementation of ALBERT(A Lite BERT for Self-supervised Learning of Language Representations)
  ☆226Updated 4 years ago
• yumeng5 / Spherical-Text-Embedding
  [NeurIPS 2019] Spherical Text Embedding
  ☆177Updated last year
• nyu-dl / bert-gen
  ☆322Updated 2 years ago
• zihangdai / mos
  ☆395Updated 6 years ago
• jiesutd / Text-Attention-Heatmap-Visualization
  Plot the vector graph of attention based text visualisation
  ☆372Updated 6 years ago
• nesl / nlp_adversarial_examples
  Implementation code for the paper "Generating Natural Language Adversarial Examples"
  ☆169Updated 5 years ago
• rowanz / swagaf
  Repository for paper "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference"
  ☆179Updated 4 years ago
• laiguokun / Funnel-Transformer
  ☆218Updated 4 years ago