New Interpreto library offers explainability for Transformer language models

A new open-source Python library named Interpreto has been released to aid in the explainability of Transformer-based language models, including BERT and larger LLMs. Developed by Antonin Poché, the library offers both attribution methods and concept-based explanations through a unified API for text classification and generation tasks. A notable feature is its comprehensive concept-based pipeline, which extends beyond typical feature-level attributions. AI

IMPACT Provides researchers and developers with tools to better understand and debug Transformer-based language models.

RANK_REASON The cluster contains an academic paper detailing a new open-source library for model explainability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Antonin Poché
• BERT
• Hugging Face
• LLM
• Transformer

• paper
• product

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