spacy-curated-transformers
Curated transformer models for spaCy pipelines
Decision gist · record as of 2026-08-14
Yes, if you need transformer models in spaCy pipelines and want a maintained integration. The package is actively developed, has low install friction, and is MIT-licensed. It requires Python >=3.9 and substantial dependencies (torch, curated-transformers), so confirm those fit your environment. No known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9 and torch installation; substantial disk space for transformer model weights.
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with last commit on 2026-03-27.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2024-09-30 (683 days) · last repo commit 2026-03-27 · 32 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,487,380 downloads/mo, #3,846 on PyPI
Alternatives
Verify before relying
pip install spacy-curated-transformers
import spacy
from spacy_curated_transformers import CuratedTransformer
nlp = spacy.load('en_core_web_sm')
# Configure transformer model in pipeline via spaCy config- Whether multi-task learning and distillation/quantization features are production-ready or experimental
- Performance characteristics and memory footprint compared to alternatives
- Specific transformer model availability and loading from Hugging Face Hub
What it is and what it does
spacy-curated-transformers is a spaCy extension that wraps the curated-transformers library to bring transformer-based NLP models into spaCy pipelines. It provides integration with spaCy's architecture system, allowing you to use pretrained models like BERT, RoBERTa, and XLM-RoBERTa as components in your NLP workflows. The package emphasizes minimal dependencies and deployment-focused features such as model distillation and quantization.
You use it by installing it alongside spaCy, then configuring transformer models in your spaCy pipeline config to power tasks like named entity recognition or text classification. It supports multi-task learning and integrates with Hugging Face Hub for model discovery. The package is maintained by Explosion AI and handles the integration between spaCy and the curated-transformers library, managing serialization, configuration, and execution of transformer models in production pipelines.
Use it for
- Build spaCy NLP pipelines powered by BERT or RoBERTa for named entity recognition
- Deploy transformer-based models in production with spaCy's serialization and config system
- Train multi-task learning models combining transformers with spaCy components
- Use XLM-RoBERTa for multilingual NLP tasks within a spaCy pipeline
- Experiment with model distillation and quantization for deployment optimization
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need transformer models in spaCy pipelines and want a maintained integration.
The package is actively developed, has low install friction, and is MIT-licensed. It requires Python >=3.9 and substantial dependencies (torch, curated-transformers), so confirm those fit your environment. No known security vulnerabilities.
Install
spacy-curated-transformers on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Actively maintained with last commit on 2026-03-27. Depends on torch and curated-transformers, which are substantial but standard ML dependencies.
Requires Python >=3.9 and torch installation; substantial disk space for transformer model weights.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install spacy-curated-transformers
import spacy
from spacy_curated_transformers import CuratedTransformer
nlp = spacy.load('en_core_web_sm')
# Configure transformer model in pipeline via spaCy configVerify before relying
- Whether multi-task learning and distillation/quantization features are production-ready or experimental
- Performance characteristics and memory footprint compared to alternatives
- Specific transformer model availability and loading from Hugging Face Hub
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagescurated-transformerscurated-tokenizersfsspecthinctorch |
| Maintenance | Actively maintained 683 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,487,380 / month, #3,846 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: spacy_curated_transformers-2.1.2-py2.py3-none-any.whl
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