interpretabilityの検索結果1 - 8 件 / 8件

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  Language Interpretability Tool (LIT) の紹介 - 機械学習 Memo φ(・ω・ )

  • 12 users
  • yukoishizaki.hatenablog.com
  • テクノロジー
  • 2020/08/24

  概要 Google Researchが、言語解釈ツール Language Interpretability Tool (LIT) を紹介する論文を出しました。NLPモデルが期待どおりに動作しない場合に、何が問題かを解明するために役立つツールだと記載されていて、便利そうだと思い試しに動かしてみたので、LITの簡単な紹介を記載します。 [2008.05122] The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models 概要 LITとは インストール LITの起動 インスタンスの起動 quickstart_sst_demo pretrained_lm_demo インスタンス起動用のスクリプト作成 Datasetクラス Modelクラス 公式ドキュメン

  • 機械学習
  • 言語
  • google

  
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  GitHub - PAIR-code/lit: The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

  • 6 users
  • github.com/PAIR-code
  • テクノロジー
  • 2020/08/14

  The Learning Interpretability Tool (🔥LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks. LIT is built to answer questions such as: What kind of examples does m

  • nlp
  • library

  
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  Learning Interpretability Tool

  • 5 users
  • pair-code.github.io
  • テクノロジー
  • 2021/04/10

  The Learning Interpretability Tool (🔥LIT) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. The Learning Interpretability Tool (🔥LIT) is for researchers and practitioners looking to understand NLP model behavior through a visual, interactive, and extensible tool. Use LIT to ask and answer questions like: What kind of examples does my model perform

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  Captum · Model Interpretability for PyTorch

  • 4 users
  • captum.ai
  • テクノロジー
  • 2019/11/22

  import numpy as np import torch import torch.nn as nn from captum.attr import IntegratedGradients class ToyModel(nn.Module): def __init__(self): super().__init__() self.lin1 = nn.Linear(3, 3) self.relu = nn.ReLU() self.lin2 = nn.Linear(3, 2) # initialize weights and biases self.lin1.weight = nn.Parameter(torch.arange(-4.0, 5.0).view(3, 3)) self.lin1.bias = nn.Parameter(torch.zeros(1,3)) self.lin2.

  

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  GitHub - pytorch/captum: Model interpretability and understanding for PyTorch

  • 4 users
  • github.com/pytorch
  • テクノロジー
  • 2019/10/12

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  • PyTorch
  • 機械学習
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  GitHub - MAIF/shapash: 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  • 4 users
  • github.com/MAIF
  • テクノロジー
  • 2021/03/10

  Shapash is a Python library designed to make machine learning interpretable and comprehensible for everyone. It offers various visualizations with clear and explicit labels that are easily understood by all. With Shapash, you can generate a Webapp that simplifies the comprehension of interactions between the model's features, and allows seamless navigation between local and global explainability.

  • 機械学習
  • Python

  
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  GitHub - sicara/tf-explain: Interpretability Methods for tf.keras models with Tensorflow 2.x

  • 4 users
  • github.com/sicara
  • テクノロジー
  • 2019/10/30

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  Interpretability in Machine Learning: An Overview

  • 3 users
  • thegradient.pub
  • テクノロジー
  • 2020/11/30

  This essay provides a broad overview of the sub-field of machine learning interpretability. While not exhaustive, my goal is to review conceptual frameworks, existing research, and future directions. I follow the categorizations used in Lipton et al.'s Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability. We'll go over ma

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