InterpretDL v0.6.0 Release

We release the version 0.6.0 of InterpretDL, with new features as follows:

• Documentation is much richer. See here.
• A new Interpreter GAInterpreter has been implemented, with a corresponding usage example. This implementation is suitable for models with self-attention in each modality, like CLIP.
• Rename the previous "tutorials" to "examples", to avoid the confusion. Examples show how to use the Interpreters and their explanation results. See tutorials for more information.
• Tutorials are provided, including Getting Started Tutorial, Input Gradient Tutorial, and four tutorials for NLP tasks using Ernie2.0 in English (on NBViewer), Bert in English (on NBViewer), BiLSTM in Chinese (on NBViewer) and Ernie1.0 in Chinese (on NBViewer)
  as examples. (For text visualizations, NBViewer gives better and colorful rendering results.)
• A taxonomy is provided for comparing the Interpreters, as follows:

Methods	Representation	Model Type	Example
LIME	Input Features	Model-Agnostic	link1 | link2
LIME with Prior	Input Features	Model-Agnostic	link
NormLIME/FastNormLIME	Input Features	Model-Agnostic	link1 | link2
LRP	Input Features	Differentiable	link
SmoothGrad	Input Features	Differentiable	link
IntGrad	Input Features	Differentiable	link
GradSHAP	Input Features	Differentiable	link
Occlusion	Input Features	Model-Agnostic	link
GradCAM/CAM	Intermediate Features	Specific: CNNs	link
ScoreCAM	Intermediate Features	Specific: CNNs	link
Rollout	Intermediate Features	Specific: Transformers	link
TAM	Intermediate Features	Specific: Transformers	link
ForgettingEvents	Dataset-Level	Differentiable	link
TIDY (Training Data Analyzer)	Dataset-Level	Differentiable	link
Consensus	Features	Cross-Model	link
Generic Attention	Input Features	Specific: Bi-Modal Transformers	link (nblink)*

* For text visualizations, NBViewer gives better and colorful rendering results.