---
title: "mteb vs awesome-llm-human-preference-datasets"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/embeddings-benchmark-mteb-vs-glgh-awesome-llm-human-preference-datasets"
tools: ["embeddings-benchmark-mteb", "glgh-awesome-llm-human-preference-datasets"]
---

# mteb vs awesome-llm-human-preference-datasets

*GraphCanon updated Aug 22, 2026*

## 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.

[mteb](https://docs.mteb.org) reports 3.4k GitHub stars, 670 forks, and 340 open issues, last pushed Aug 21, 2026. [awesome-llm-human-preference-datasets](https://github.com/glgh/awesome-llm-human-preference-datasets) has 390 stars, 19 forks, and 0 open issues, last pushed Oct 4, 2023. Figures are from public GitHub metadata via [mteb's repository](https://github.com/embeddings-benchmark/mteb) and [awesome-llm-human-preference-datasets's repository](https://github.com/glgh/awesome-llm-human-preference-datasets).

| | [mteb](/tools/embeddings-benchmark-mteb.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Tagline | State-of-the-art evaluation of embeddings across languages and modalities | Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval |
| Stars | 3,400 | 390 |
| Forks | 670 | 19 |
| Open issues | 340 | 0 |
| Language | Python | - |
| Adopt for | MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license. | 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 | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [mteb](/tools/embeddings-benchmark-mteb.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 0d | 1036d |
| Open issues (now) | 340 | 0 |
| Stars delta | +36 (30d) | Unknown |
| Open issues delta | +31 (30d) | Unknown |
| Owner type | Organization | User |
| Full report | [trust report](/tools/embeddings-benchmark-mteb/trust.md) | [trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust.md) |

## Decision facts: mteb

- **Adopt for:** MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.

## Decision facts: awesome-llm-human-preference-datasets

- **Adopt for:** 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

## Choose when

### 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.

### 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 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 NOT to use awesome-llm-human-preference-datasets

- NLP，LLM、，。

## 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 (3,400 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](/tools/embeddings-benchmark-mteb/alternatives) and [awesome-llm-human-preference-datasets alternatives](/tools/glgh-awesome-llm-human-preference-datasets/alternatives) ([mteb markdown twin](/tools/embeddings-benchmark-mteb/alternatives.md), [awesome-llm-human-preference-datasets markdown twin](/tools/glgh-awesome-llm-human-preference-datasets/alternatives.md)), 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](/compare/embeddings-benchmark-mteb-vs-glgh-awesome-llm-human-preference-datasets.md) 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](/tools/embeddings-benchmark-mteb/trust); [awesome-llm-human-preference-datasets trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=embeddings-benchmark-mteb`](/api/graphcanon/graph?tool=embeddings-benchmark-mteb)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_