Comparison
ALERT vs transformers
Verdict
Pick ALERT when license: ALERT is Other, transformers is Apache-2.0; pick transformers when license: transformers is Apache-2.0, ALERT is Other.
Markdown twin · ALERT alternatives · transformers alternatives
GraphCanon updated today
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Trust & integrity
| Signal | ALERT | transformers |
|---|---|---|
| Maintenance | Dormant (663d since push) As of today · github_public_v1 | Very active (0d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-15 As of today · osv@v1 | No lockfile (source not queried) As of 4d · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- ALERT
- Official repository for the paper "ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming"
- transformers
- Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models
Stars
- ALERT
- 60
- transformers
- 162k
Forks
- ALERT
- 9
- transformers
- 34k
Open issues
- ALERT
- 0
- transformers
- 2.5k
Language
- ALERT
- Python
- transformers
- Python
Adopt for
- ALERT
- -
- transformers
- Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3
Persona
- ALERT
- -
- transformers
- -
Runtime
- ALERT
- -
- transformers
- -
License
- ALERT
- Other
- transformers
- Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.
Last pushed
- ALERT
- Sep 20, 2024
- transformers
- Jul 11, 2026
Categories
- ALERT
- Computer Vision, LLM Frameworks, Model Training
- transformers
- Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- ALERT
- Dormant (18%)
- transformers
- Very active (96%)
Days since push
- ALERT
- 663d
- transformers
- 0d
Open issues (now)
- ALERT
- 0
- transformers
- 2.5k
OSV dependency advisories
- ALERT
- No published findings from this source as of 2026-07-15
- transformers
- No lockfile (source not queried)
Full report
- ALERT
- Trust report
- transformers
- Trust report
Choose ALERT if…
- License: ALERT is Other, transformers is Apache-2.0.
- Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection.
- Leaner open-issue backlog (0).
When NOT to use ALERT
- Last GitHub push was 663 days ago (dormant maintenance, Sep 20, 2024). Validate activity before betting a new project on ALERT.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Choose transformers if…
- License: transformers is Apache-2.0, ALERT is Other.
- Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+.
- Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing.
- Also covers Inference & Serving, Speech & Audio.
- The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.
When NOT to use transformers
- If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable.
- It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Babelscape/ALERT) · observed Jul 15, 2026
- GitHub forks (Babelscape/ALERT) · observed Jul 15, 2026
- Last push (Babelscape/ALERT) · observed Sep 20, 2024
- License file (Other) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (huggingface/transformers) · observed Jul 11, 2026
- GitHub forks (huggingface/transformers) · observed Jul 11, 2026
- Last push (huggingface/transformers) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ALERT 60 · transformers 162k (synced Jul 15, 2026).
Common questions
- What is the difference between ALERT and transformers?
- ALERT: Official repository for the paper "ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming". transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. See the comparison table for live GitHub stats and shared categories.
- When should I choose ALERT over transformers?
- Choose ALERT over transformers when License: ALERT is Other, transformers is Apache-2.0; Tags unique to ALERT: ai, artificial-intelligence, benchmark, bias-detection; Leaner open-issue backlog (0).
- When should I choose transformers over ALERT?
- Choose transformers over ALERT when License: transformers is Apache-2.0, ALERT is Other; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing; Also covers Inference & Serving, Speech & Audio; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.
- When should I avoid ALERT?
- Last GitHub push was 663 days ago (dormant maintenance, Sep 20, 2024). Validate activity before betting a new project on ALERT. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- When should I avoid transformers?
- If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
- Is ALERT or transformers more popular on GitHub?
- transformers has more GitHub stars (162,482 vs 60). Stars measure visibility, not whether either tool fits your constraints.
- Are ALERT and transformers open source?
- Yes - both are open-source projects on GitHub (ALERT: Other, transformers: Apache-2.0).
- Where can I find alternatives to ALERT or transformers?
- GraphCanon lists graph-backed alternatives at ALERT alternatives and transformers alternatives (ALERT markdown twin, transformers markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, ALERT or transformers?
- ALERT: Dormant. transformers: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for ALERT and transformers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ALERT trust report; transformers trust report.