transformers vs human-eval

Common questions

What is the difference between transformers and human-eval?

transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. human-eval: Code for the paper "Evaluating Large Language Models Trained on Code". See the comparison table for live GitHub stats and shared categories.

When should I choose transformers over human-eval?

Choose transformers over human-eval when License: transformers is Apache-2.0, human-eval is MIT; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: pretrained models, deep-learning, machine-learning, natural-language-processing; Also covers Speech & Audio, Computer Vision, Inference & Serving; 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 choose human-eval over transformers?

Choose human-eval over transformers when License: human-eval is MIT, transformers is Apache-2.0; Also covers Evaluation & Observability; Leaner open-issue backlog (42).

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.

When should I avoid human-eval?

Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval. 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. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

Is transformers or human-eval more popular on GitHub?

transformers has more GitHub stars (162,482 vs 3,294). Stars measure visibility, not whether either tool fits your constraints.

Are transformers and human-eval open source?

Yes - both are open-source projects on GitHub (transformers: Apache-2.0, human-eval: MIT).

Where can I find alternatives to transformers or human-eval?

GraphCanon lists graph-backed alternatives at transformers alternatives and human-eval alternatives (transformers markdown twin, human-eval 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, transformers or human-eval?

transformers: Very active. human-eval: 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 transformers and human-eval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: transformers trust report; human-eval trust report.