Comparison
mteb vs awesome-llm-human-preference-datasets
Verdict
Pick mteb if mTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license; pick awesome-llm-human-preference-datasets if awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被.
Markdown twin · mteb alternatives · awesome-llm-human-preference-datasets alternatives
GraphCanon updated 2w
vs
awesome-llm-human-preference-datasets
glgh/awesome-llm-human-preference-datasets
★ 390pushed Oct 4, 2023
Trust & integrity
| Signal | mteb | awesome-llm-human-preference-datasets |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1mo · github_public_v1 | Dormant (1036d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- mteb
- State-of-the-art evaluation of embeddings across languages and modalities
- awesome-llm-human-preference-datasets
- Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval
Stars
- mteb
- 3.4k
- awesome-llm-human-preference-datasets
- 390
Forks
- mteb
- 645
- awesome-llm-human-preference-datasets
- 19
Open issues
- mteb
- 309
- awesome-llm-human-preference-datasets
- 0
Language
- mteb
- Python
- awesome-llm-human-preference-datasets
- -
Adopt for
- mteb
- MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
- awesome-llm-human-preference-datasets
- awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被
Persona
- mteb
- -
- awesome-llm-human-preference-datasets
- -
Runtime
- mteb
- -
- awesome-llm-human-preference-datasets
- -
License
- mteb
- Apache-2.0
- awesome-llm-human-preference-datasets
- MIT
Last pushed
- mteb
- Jul 22, 2026
- awesome-llm-human-preference-datasets
- Oct 4, 2023
Categories
- mteb
- Evaluation & Observability
- awesome-llm-human-preference-datasets
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- mteb
- Very active (96%)
- awesome-llm-human-preference-datasets
- Dormant (18%)
Days since push
- mteb
- 0d
- awesome-llm-human-preference-datasets
- 1036d
Open issues (now)
- mteb
- 309
- awesome-llm-human-preference-datasets
- 0
Owner type
- mteb
- Organization
- awesome-llm-human-preference-datasets
- User
Full report
- mteb
- Trust report
- awesome-llm-human-preference-datasets
- Trust report
Choose mteb if…
- License: mteb is Apache-2.0, awesome-llm-human-preference-datasets is MIT.
- Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings.
- 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.
Choose awesome-llm-human-preference-datasets if…
- License: awesome-llm-human-preference-datasets is MIT, mteb is Apache-2.0.
- Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences.
- Also covers Model Training.
- 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。
When NOT to use awesome-llm-human-preference-datasets
- NLP，LLM、，。
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (embeddings-benchmark/mteb) · observed Jul 22, 2026
- GitHub forks (embeddings-benchmark/mteb) · observed Jul 22, 2026
- Last push (embeddings-benchmark/mteb) · observed Jul 22, 2026
- License file (Apache-2.0) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (glgh/awesome-llm-human-preference-datasets) · observed Aug 6, 2026
- GitHub forks (glgh/awesome-llm-human-preference-datasets) · observed Aug 6, 2026
- Last push (glgh/awesome-llm-human-preference-datasets) · observed Oct 4, 2023
- License file (MIT) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mteb 3.4k · awesome-llm-human-preference-datasets 390 (synced Jul 22, 2026).
Common questions
- What is the difference between mteb and awesome-llm-human-preference-datasets?
- mteb: State-of-the-art evaluation of embeddings across languages and modalities. awesome-llm-human-preference-datasets: Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval. See the comparison table for live GitHub stats and shared categories.
- When should I choose mteb over awesome-llm-human-preference-datasets?
- Choose mteb over awesome-llm-human-preference-datasets when License: mteb is Apache-2.0, awesome-llm-human-preference-datasets is MIT; Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings; 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 choose awesome-llm-human-preference-datasets over mteb?
- Choose awesome-llm-human-preference-datasets over mteb when License: awesome-llm-human-preference-datasets is MIT, mteb is Apache-2.0; Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences; Also covers Model Training; 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。.
- 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.
- When should I avoid awesome-llm-human-preference-datasets?
- NLP，LLM、，。
- Is mteb or awesome-llm-human-preference-datasets more popular on GitHub?
- mteb has more GitHub stars (3,364 vs 390). Stars measure visibility, not whether either tool fits your constraints.
- Are mteb and awesome-llm-human-preference-datasets open source?
- Yes - both are open-source projects on GitHub (mteb: Apache-2.0, awesome-llm-human-preference-datasets: MIT).
- Where can I find alternatives to mteb or awesome-llm-human-preference-datasets?
- GraphCanon lists graph-backed alternatives at mteb alternatives and awesome-llm-human-preference-datasets alternatives (mteb markdown twin, awesome-llm-human-preference-datasets 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, mteb or awesome-llm-human-preference-datasets?
- mteb: Very active. awesome-llm-human-preference-datasets: Dormant. 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 mteb and awesome-llm-human-preference-datasets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mteb trust report; awesome-llm-human-preference-datasets trust report.