multi-label-sentiment-classifier VS Transformer-Explainability

Compare multi-label-sentiment-classifier vs Transformer-Explainability and see what are their differences.

multi-label-sentiment-classifier

How to build a multi-label sentiment classifiers with Tez and PyTorch (by ahmedbesbes)
Pytorch huggingface huggingface-transformers squeezebert bert-model nlproc NLP multi-label-classification tez goemotions
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Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks. (by hila-chefer)
Deep Learning vision-transformer bert-model Bert explainability transformer-interpretability perturbation attention-visualization visualize-classifications vit attention-matrix cvpr2021
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multi-label-sentiment-classifier Transformer-Explainability
1 1
17 1,660
- -
1.8 0.0
about 3 years ago 3 months ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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multi-label-sentiment-classifier

Posts with mentions or reviews of multi-label-sentiment-classifier. We have used some of these posts to build our list of alternatives and similar projects.

Transformer-Explainability

Posts with mentions or reviews of Transformer-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-25.
  • [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
    2 projects | /r/MachineLearning | 25 Apr 2021
    Not exactly the same but since you mentioned using ViT's attention outputs as a 2D feature map for the CAM you can consider this paper (Transformer Interpretability Beyond Attention Visualization) where they study the question of how to choose/mix the attention scores in a way that can be visualized (so similar to the CAMs). Maybe it can lead to better results. https://arxiv.org/abs/2012.09838 https://github.com/hila-chefer/Transformer-Explainability

What are some alternatives?

When comparing multi-label-sentiment-classifier and Transformer-Explainability you can also consider the following projects:

ganbert-pytorch - Enhancing the BERT training with Semi-supervised Generative Adversarial Networks in Pytorch/HuggingFace

pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

detoxify - Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].

shap - A game theoretic approach to explain the output of any machine learning model.

aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.

T2T-ViT - ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

predict-subreddit - NLP model that predicts subreddit based on the title of a post

HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

huggingpics - 🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

tf-metal-experiments - TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

browser-ml-inference - Edge Inference in Browser with Transformer NLP model

deep-text-recognition-benchmark - PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR)

multi-label-sentiment-classifier vs ganbert-pytorch Transformer-Explainability vs pytorch-grad-cam multi-label-sentiment-classifier vs detoxify Transformer-Explainability vs shap multi-label-sentiment-classifier vs aws-lambda-docker-serverless-inference Transformer-Explainability vs T2T-ViT multi-label-sentiment-classifier vs predict-subreddit Transformer-Explainability vs HugsVision multi-label-sentiment-classifier vs huggingpics Transformer-Explainability vs tf-metal-experiments multi-label-sentiment-classifier vs browser-ml-inference Transformer-Explainability vs deep-text-recognition-benchmark