Comparison
long-context-attention vs GLiNER
Verdict
Pick long-context-attention if long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference; pick GLiNER if gLiNER is ideal for extracting named entities from text with minimal computational resources.
Markdown twin · long-context-attention alternatives · GLiNER alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | long-context-attention | GLiNER |
|---|---|---|
| Maintenance | Steady (65d since push) As of 3w · github_public_v1 | Active (7d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- long-context-attention
- Unified Sequence Parallel Attention for Long Context Transformers
- GLiNER
- Generalist and Lightweight Model for Named Entity Recognition
Stars
- long-context-attention
- 682
- GLiNER
- 3.5k
Forks
- long-context-attention
- 81
- GLiNER
- 299
Open issues
- long-context-attention
- 13
- GLiNER
- 96
Language
- long-context-attention
- Python
- GLiNER
- Python
Adopt for
- long-context-attention
- long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.
- GLiNER
- GLiNER is ideal for extracting named entities from text with minimal computational resources.
Persona
- long-context-attention
- -
- GLiNER
- -
Runtime
- long-context-attention
- -
- GLiNER
- -
License
- long-context-attention
- Apache-2.0
- GLiNER
- Apache-2.0
Last pushed
- long-context-attention
- May 21, 2026
- GLiNER
- Aug 10, 2026
Categories
- long-context-attention
- Inference & Serving, Model Training
- GLiNER
- Data & Retrieval, Model Training
Trust and health
Maintenance
- long-context-attention
- Steady (60%)
- GLiNER
- Active (82%)
Days since push
- long-context-attention
- 65d
- GLiNER
- 7d
Open issues (now)
- long-context-attention
- 13
- GLiNER
- 96
Stars delta
- long-context-attention
- Unknown
- GLiNER
- +143 (30d)
Open issues delta
- long-context-attention
- Unknown
- GLiNER
- -1 (30d)
OSV dependency advisories
- long-context-attention
- No lockfile (source not queried)
- GLiNER
- Published findings
Full report
- long-context-attention
- Trust report
- GLiNER
- Trust report
Shared compatibility
- Python · long-context-attention: Python runtime · GLiNER: Python runtime
Choose long-context-attention if…
- Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training.
- Also covers Inference & Serving.
- When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.
When NOT to use long-context-attention
- If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits.
- When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.
Choose GLiNER if…
- Tags unique to GLiNER: information-extraction, large language models, named-entity-recognition, natural-language-processing.
- Also covers Data & Retrieval.
- When you need a lightweight solution for named entity recognition across various languages
When NOT to use GLiNER
- If high precision in niche specializations like medical terms or rare proper nouns is required
- In scenarios demanding heavy customization beyond basic named entity recognition capabilities
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (feifeibear/long-context-attention) · observed Jul 25, 2026
- GitHub forks (feifeibear/long-context-attention) · observed Jul 25, 2026
- Last push (feifeibear/long-context-attention) · observed May 21, 2026
- License file (Apache-2.0) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (urchade/GLiNER) · observed Aug 18, 2026
- GitHub forks (urchade/GLiNER) · observed Aug 18, 2026
- Last push (urchade/GLiNER) · observed Aug 10, 2026
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: long-context-attention 682 · GLiNER 3.5k (synced Jul 25, 2026).
Common questions
- What is the difference between long-context-attention and GLiNER?
- long-context-attention: Unified Sequence Parallel Attention for Long Context Transformers. GLiNER: Generalist and Lightweight Model for Named Entity Recognition. See the comparison table for live GitHub stats and shared categories.
- When should I choose long-context-attention over GLiNER?
- Choose long-context-attention over GLiNER when Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training; Also covers Inference & Serving; When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.
- When should I choose GLiNER over long-context-attention?
- Choose GLiNER over long-context-attention when Tags unique to GLiNER: information-extraction, large language models, named-entity-recognition, natural-language-processing; Also covers Data & Retrieval; When you need a lightweight solution for named entity recognition across various languages.
- When should I avoid long-context-attention?
- If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits. When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.
- When should I avoid GLiNER?
- If high precision in niche specializations like medical terms or rare proper nouns is required In scenarios demanding heavy customization beyond basic named entity recognition capabilities
- Is long-context-attention or GLiNER more popular on GitHub?
- GLiNER has more GitHub stars (3,545 vs 682). Stars measure visibility, not whether either tool fits your constraints.
- Are long-context-attention and GLiNER open source?
- Yes - both are open-source projects on GitHub (long-context-attention: Apache-2.0, GLiNER: Apache-2.0).
- Where can I find alternatives to long-context-attention or GLiNER?
- GraphCanon lists graph-backed alternatives at long-context-attention alternatives and GLiNER alternatives (long-context-attention markdown twin, GLiNER 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, long-context-attention or GLiNER?
- long-context-attention: Steady. GLiNER: 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 long-context-attention and GLiNER?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: long-context-attention trust report; GLiNER trust report.