Comparison
Awesome-Datasets-Hub vs mteb
Verdict
Pick Awesome-Datasets-Hub if awesome-Datasets-Hub offers a curated selection of datasets focusing particularly on medical AI, NLP, and multimodal applications, essential for training large language models; pick mteb if mTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
Markdown twin · Awesome-Datasets-Hub alternatives · mteb alternatives
GraphCanon updated Sep 20, 2026
12views this month
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Trust & integrity
| Signal | Awesome-Datasets-Hub | mteb |
|---|---|---|
| Maintenance | Slowing (91d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Published findings As of Sep 27, 2026 · openssf-scorecard@v1 |
Tagline
- Awesome-Datasets-Hub
- Curated collection of datasets for Large Language Models (LLMs)
- mteb
- State-of-the-art evaluation of embeddings across languages and modalities
Stars
- Awesome-Datasets-Hub
- 148
- mteb
- 3.4k
Forks
- Awesome-Datasets-Hub
- 40
- mteb
- 704
Open issues
- Awesome-Datasets-Hub
- 1
- mteb
- 324
Language
- Awesome-Datasets-Hub
- -
- mteb
- Python
Adopt for
- Awesome-Datasets-Hub
- Awesome-Datasets-Hub offers a curated selection of datasets focusing particularly on medical AI, NLP, and multimodal applications, essential for training large language models.
- mteb
- MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
Persona
- Awesome-Datasets-Hub
- -
- mteb
- -
Runtime
- Awesome-Datasets-Hub
- -
- mteb
- -
License
- Awesome-Datasets-Hub
- -
- mteb
- Apache-2.0
Last pushed
- Awesome-Datasets-Hub
- Jun 20, 2026
- mteb
- Sep 19, 2026
Categories
- Awesome-Datasets-Hub
- Data & Retrieval, Evaluation & Observability
- mteb
- Evaluation & Observability
Trust and health
Maintenance
- Awesome-Datasets-Hub
- Slowing (36%)
- mteb
- Very active (96%)
Days since push
- Awesome-Datasets-Hub
- 91d
- mteb
- 0d
Open issues (now)
- Awesome-Datasets-Hub
- 1
- mteb
- 324
Stars delta
- Awesome-Datasets-Hub
- +2 (30d)
- mteb
- +63 (30d)
Open issues delta
- Awesome-Datasets-Hub
- 0 (30d)
- mteb
- +15 (30d)
Owner type
- Awesome-Datasets-Hub
- User
- mteb
- Organization
OpenSSF Scorecard
- Awesome-Datasets-Hub
- Not queried
- mteb
- Published findings
Full report
- Awesome-Datasets-Hub
- Trust report
- mteb
- Trust report
Choose Awesome-Datasets-Hub if…
- Tags unique to Awesome-Datasets-Hub: code-generation, instruction-tuning, llm-evaluation, medical-ai.
- Also covers Data & Retrieval.
- You need comprehensive datasets for clinical evaluation or specialized biomedical QA tasks.
When NOT to use Awesome-Datasets-Hub
- Your focus is on domains outside of healthcare and medicine, where this tool might not provide adequate data diversity.
- You seek real-time dataset updates, as the specific update cadence for Awesome-Datasets-Hub isn't publicly specified.
Choose mteb if…
- Tags unique to mteb: bitext-mining, clustering, embeddings, information retrieval.
- mteb ships Docker support for self-hosted deployment.
- You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
When NOT to use mteb
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope.
- You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ahammadmejbah/Awesome-Datasets-Hub) · observed Sep 20, 2026
- GitHub forks (ahammadmejbah/Awesome-Datasets-Hub) · observed Sep 20, 2026
- Last push (ahammadmejbah/Awesome-Datasets-Hub) · observed Jun 20, 2026
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (embeddings-benchmark/mteb) · observed Sep 20, 2026
- GitHub forks (embeddings-benchmark/mteb) · observed Sep 20, 2026
- Last push (embeddings-benchmark/mteb) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Datasets-Hub 148 · mteb 3.4k (synced Sep 20, 2026).
Common questions
- What is the difference between Awesome-Datasets-Hub and mteb?
- Awesome-Datasets-Hub: Curated collection of datasets for Large Language Models (LLMs). mteb: State-of-the-art evaluation of embeddings across languages and modalities. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Datasets-Hub over mteb?
- Choose Awesome-Datasets-Hub over mteb when Tags unique to Awesome-Datasets-Hub: code-generation, instruction-tuning, llm-evaluation, medical-ai; Also covers Data & Retrieval; You need comprehensive datasets for clinical evaluation or specialized biomedical QA tasks.
- When should I choose mteb over Awesome-Datasets-Hub?
- Choose mteb over Awesome-Datasets-Hub when Tags unique to mteb: bitext-mining, clustering, embeddings, information retrieval; mteb ships Docker support for self-hosted deployment; You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
- When should I avoid Awesome-Datasets-Hub?
- Your focus is on domains outside of healthcare and medicine, where this tool might not provide adequate data diversity. You seek real-time dataset updates, as the specific update cadence for Awesome-Datasets-Hub isn't publicly specified.
- When should I avoid mteb?
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope. You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
- Is Awesome-Datasets-Hub or mteb more popular on GitHub?
- mteb has more GitHub stars (3,427 vs 148). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Datasets-Hub and mteb open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Datasets-Hub or mteb?
- GraphCanon lists graph-backed alternatives at Awesome-Datasets-Hub alternatives and mteb alternatives (Awesome-Datasets-Hub markdown twin, mteb 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, Awesome-Datasets-Hub or mteb?
- Awesome-Datasets-Hub: Slowing. mteb: 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 Awesome-Datasets-Hub and mteb?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Datasets-Hub trust report; mteb trust report.