inseq-team / inseq
Interpretability for sequence generation models 🐛 🔍
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Feature attribution of sequence generation for most ForConditionalGeneration (encoder-decoder) and ForCausalLM (decoder-only) models from 🤗 Transformers
- Support for multiple feature attribution methods, extending the ones supported by Captum
- Post-processing, filtering and merging of attribution maps via Aggregator classes.
- Attribution visualization in notebooks, browser and command line.
- Efficient attribution of single examples or entire 🤗 datasets with the Inseq CLI.
Get started
cd inseq
make uv-download # Download and install the uv package manager
make install # Installs the package and all dependencies via uv sync- Maintenance
- Active
- Last commit 21 days ago
- Stars
- 477
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.7.0
- Released 9 months ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 77% closed
- 22 still open
- Pull requests
- 195 merged
- 7 waiting
- Releases
- 9
- 1 in the last year
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More: languages, topics and project record
Languages
- Python 100%
- Makefile <1%
- Dockerfile <1%
Topics
- attribution-methods
- captum
- deep-learning
- explainable-ai
- generative-ai
- huggingface
- interpretability
- language-generation
- language-model
- large-language-models
- natural-language-processing
- sequence-to-sequence
Project record
- JobOther AI tools
- StatusListed
- Commits480
- Forks43
- Watching8
- Stars in 28 days0
- CreatedSep 14, 2021
- First seen by usNov 7, 2026
- CheckedNov 8, 2026