Text Classification
Scikit-learn
Joblib
ai-systems
capabilities
reasoning
planning
memory
agents
world-models
verification
reliability
Instructions to use ai-systems/capability-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ai-systems/capability-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("ai-systems/capability-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 2,407 Bytes
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license: apache-2.0
library_name: sklearn
pipeline_tag: text-classification
tags:
- ai-systems
- capabilities
- reasoning
- planning
- memory
- agents
- world-models
- verification
- reliability
---
# Capability Classifier
**Capability Classifier** is a lightweight reference model that maps short AI task descriptions to practical capability categories.
It is published under the **ai-systems** handle as a transparent demonstration model for AI-system analysis.
## Capability Labels
- `adaptation`
- `agents`
- `coding`
- `memory`
- `multimodal`
- `planning`
- `reasoning`
- `reliability`
- `science`
- `tool-use`
- `verification`
- `world-modeling`
## Examples
Input:
```text
Break a complex objective into subtasks and replan after failure.
```
Expected category:
```text
planning
```
Input:
```text
Predict how the environment will change before acting.
```
Expected category:
```text
world-modeling
```
Input:
```text
Run tests before accepting generated code.
```
Expected category:
```text
verification
```
## Usage
```python
from joblib import load
classifier = load("capability-classifier.joblib")
text = "Use a browser and API to complete the task"
prediction = classifier.predict([text])[0]
print(prediction)
```
## Model Architecture
The reference model uses:
- TF-IDF text features
- unigram and bigram features
- logistic regression classification
The model is intentionally small so the classification approach remains easy to inspect and reproduce.
## Training Data
The model was trained on a small curated set of short AI-task descriptions covering capability areas such as:
- reasoning
- coding
- planning
- memory
- tool use
- agents
- multimodal understanding
- world modeling
- verification
- reliability
- adaptation
- science
## Intended Use
Suitable for:
- capability explorers
- educational tools
- lightweight taxonomy experiments
- AI-system documentation
- prototyping
- simple routing demos
## Limitations
This is a **reference model**, not a benchmark and not a production-grade classifier.
It was trained on a small curated dataset. Predictions outside the covered task descriptions may be unreliable.
The model should not be used for medical, legal, financial, safety-critical, or other high-impact decisions.
## Related Dataset
`ai-systems/ai-system-patterns`
## Related Model
`ai-systems/system-router`
## License
Apache-2.0
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