Show HN: Local pretrained classifiers, GPU not needed

Pretrained classifiers you can run and retrain on CPU — text, game state, or any numeric features.

Try it

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-serve

Open http://localhost:8400, pick a classifier, and paste one of these:

Classifier	Try this text
banking77	I was charged twice for the same transaction
sms_spam	WINNER! You have been selected for a free cruise. Reply YES to claim.
ag_news	The Federal Reserve raised interest rates by 25 basis points on Wednesday

Pretrained capabilities

15 classifiers ship with the package, including a real-time Doom game-state classifier and an inbox router. Weights are logistic regression coefficients (derived model parameters, not copies of training data). Source datasets and licenses are documented in ATTRIBUTION.md.

Task	What it does	Classes	Test Acc	Test F1
sms_spam	SMS spam detection	2	99.1%	98.0%
dbpedia	Wikipedia article category	14	96.0%	95.9%
imdb	Movie review sentiment (long text)	2	94.8%	94.8%
banking77	Banking customer intent	77	94.3%	94.3%
ag_news	News topic (world/sports/business/tech)	4	90.5%	90.5%
sst2	Movie review sentiment	2	90.1%	90.1%
clinc_oos	Voice assistant intent + out-of-scope	151	88.4%	92.1%
massive_intent	Smart home voice commands	60	88.1%	86.4%
tweet_eval_offensive	Offensive language	2	81.0%	74.8%
tweet_eval_emotion	Tweet emotion	4	78.1%	74.7%
emotion	Text emotion (6 emotions)	6	75.5%	67.8%
tweet_eval_sentiment	Tweet sentiment (3-way)	3	66.2%	65.7%
snli	Natural language inference	3	65.6%	65.2%
doom_fire	Doom game-state decisions (fire/turn)	3	100.0%	100.0%
inbox_router	Email inbox routing (work/family/promo/notification)	4	70.8%	70.8%

Test accuracy on held-out splits. Details in data/eval_results/benchmark.json. doom_fire operates on numeric feature vectors (24 game-state features) instead of text — see examples/doom/. inbox_router is a demo classifier trained on 24 examples — see the live demo in the playground.

Weaknesses: SNLI (65.6%) and tweet_eval_sentiment (66.2%) are below what task-specific models achieve. Emotion (75.5%) has limited class coverage. Probabilities are uncalibrated.

Train a custom classifier

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-train --example --save-dir my_models

Loaded 24 examples from reviews.csv
Training 'reviews': 24 examples, 2 classes
  Split: 19 train, 5 test
  Test accuracy: 100.0%
Saved to my_models/reviews/

--example uses a bundled 24-row product review CSV. To bring your own:

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-train --input your_data.csv --text-col text --label-col label \
  --task-id your_task --save-dir my_models

Supports .csv, .tsv, and .jsonl.

Serve a custom model

JEFFY_PACK_DIR=my_models uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-serve

curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "The battery life is amazing", "task": "reviews"}'
# → {"label": "positive", "confidence": 0.87, ...}

Install from source

git clone https://github.com/nicobrenner/jeffy.git
cd jeffy

# With uv (recommended)
uv venv && uv pip install -e .

# Or with pip
python -m venv .venv && source .venv/bin/activate
pip install -e .

The first prediction downloads the shared encoder (bge-large-en-v1.5, ~1.2 GB, cached afterward).

API

Python

from jeffy.engine import Engine

engine = Engine()
engine.load()

# List available classifiers
for name, cap in engine.capabilities.items():
    print(f"{name}: {cap.description} ({cap.n_classes} classes)")

# Classify text
result = engine.predict("banking77", "I was charged twice for the same transaction")
print(result["label"])          # "transaction_charged_twice"
print(result["confidence"])     # 0.999
print(result["probabilities"])  # {"transaction_charged_twice": 0.999, ...}

SDK walkthrough

HTTP

# Banking intent
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "I was charged twice for the same transaction", "task": "banking77"}'
# → {"label": "transaction_charged_twice", "confidence": 0.999, ...}

# Spam detection
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "WINNER! You have been selected for a free cruise. Reply YES to claim.", "task": "sms_spam"}'
# → {"label": "spam", "confidence": 0.91, ...}

# News topic
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "The Federal Reserve raised interest rates by 25 basis points on Wednesday", "task": "ag_news"}'
# → {"label": "Business", "confidence": 0.86, ...}

List capabilities

curl -s http://localhost:8400/v1/capabilities | python3 -c "
import json, sys
for c in json.load(sys.stdin)['capabilities']:
    print(f\"{c['task_id']:25s} {c['n_classes']:3d} classes  {c['test_accuracy']:.1%}  {c['name']}\")"

Per-capability metadata

Each shipped classifier has a manifest.json with label names, source dataset, HuggingFace path, stated license, encoder identity, training/test counts, and integrity hashes.

curl -s http://localhost:8400/v1/capabilities/banking77 | python3 -m json.tool

Train from Python

from jeffy.train import train_classifier

clf = train_classifier(
    texts=["great product!", "terrible service", "fast shipping", "broken on arrival"],
    labels=["positive", "negative", "positive", "negative"],
    task_id="my_reviews",
)

result = clf.predict("the quality exceeded my expectations")
print(result["label"])  # "positive"

clf.save("my_models")

Tuning options

Parameter	Default	Description
`C`	0.01	How aggressively the model fits your data. Low (0.001) = conservative, keeps predictions closer to "I'm not sure." High (1.0) = trusts individual training examples more. If the model is great on training data but bad on new data (overfitting), lower C.
`test_size`	0.2	What fraction of your data to hold back for testing. With 100 examples at 0.2, it trains on 80 and tests on 20. Set to 0 to train on everything (useful when you have very little data and will test manually).
`cv_folds`	3	Cross-validation: splits your training data into 3 parts, trains on 2 and tests on 1, rotates three times, averages the scores. Gives a more reliable accuracy estimate than a single split. Set to 0 to skip (faster, less reliable estimate).

Start with the defaults. With <50 examples per class, expect noisy estimates.

Reproduce the evaluation

# Install with build dependencies
uv pip install -e ".[build]"
# or: pip install -e ".[build]"

# Retrain all 13 heads from source datasets (~40 min, downloads ~5 GB)
jeffy-build --out data/model_pack

# Evaluate on held-out test sets with tuned baselines
jeffy-evaluate --baselines --latency --device cpu

Evaluation notes

SST-2: Evaluated on validation split (official test labels are not public).
SMS Spam: Random split (test_size=0.2, seed=42); no standard benchmark split.
SNLI: Input encoded as premise [SEP] hypothesis. Label -1 filtered.
CLINC-OOS: 151 classes including out-of-scope. In-scope accuracy 96.5%, OOS detection 51.7%.
MASSIVE: English only (config en).

Deployment

Component	Size	Required for
Jeffy package (wheel)	1.5 MB	Always (includes all 13 heads)
Encoder (bge-large-en-v1.5)	~1.2 GB	Inference (downloaded on first use)
`datasets` package	~100 MB	Retraining from HuggingFace only

Runtime memory: ~2 GB (encoder loaded once, shared across all heads).

Latency (CPU, single example, Linux aarch64):

Stage	p50	Notes
Embedding	50–80 ms	Dominates; varies with input length
Classifier	<1 ms	Negligible
Total	50–80 ms	End-to-end

Model security

Bundled pretrained artifacts use numpy .npz format (portable, no pickle). Custom-trained models also save a joblib pickle backup. Only load custom pickle artifacts from trusted sources. Each artifact's manifest.json includes integrity hashes verified on load.

Provenance and licensing

Jeffy code is MIT-licensed. Head artifacts are derived from public datasets; redistribution permissions have not been independently verified for all sources. See ATTRIBUTION.md for per-dataset license status.

Dataset	Stated license
banking77, massive_intent, sms_spam	CC BY 4.0
clinc_oos	CC BY 3.0
dbpedia	CC BY-SA 3.0
snli	CC BY-SA 4.0
ag_news, imdb	Academic / non-commercial
sst2	Stanford academic license
emotion	Academic
tweet_eval_*	Twitter TOS / academic

The encoder (bge-large-en-v1.5) is MIT-licensed.

Tested

Verified with clean-environment wheel and sdist install on Linux aarch64, Python 3.12, scikit-learn 1.9+, sentence-transformers 6.1+, numpy 2.5+. Pretrained artifacts use numpy .npz format, avoiding sklearn version coupling.

What's not included

No zero-shot / general classification. Each task needs a trained head. Unknown tasks return an error.
No LLM fallback. This release is pure embedding + classifier.
No automatic task routing. You must specify which classifier to use.
No hosted service. Runs locally only for now.

Roadmap

Status	Milestone
Available	Pretrained classifier library (14 text + 1 game-state), SDK/API, custom training from CSV/JSONL
Available	PyPI package (`pip install jeffy-classify`), non-text classifiers (numeric feature vectors)
Available	Classifier catalog UI with model detail pages, live try-it forms, and usage snippets
Available	Live Doom demo — real-time VizDoom classifier streaming in the playground
Available	Inbox classifier demo — animated training + classification workflow
Next	Live demos for more classifiers, broader catalog released in verified batches
Next	Landing page
Planned	Automatic routing among supported classifiers
Planned	Optional local/API LLM fallback for unsupported tasks
Planned	Hosted classifier catalog and decision-routing service
Exploring	Assisted labeling, retraining from corrections, classifier sharing

Suggestions for datasets, capabilities, or workflows are welcome as issues.

Pretrained classifiers you can run and retrain on CPU — text, game state, or any numeric features.

Try it

Install uv, then:

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-serve

Open http://localhost:8400, pick a classifier, and paste one of these:

Classifier	Try this text
banking77	I was charged twice for the same transaction
sms_spam	WINNER! You have been selected for a free cruise. Reply YES to claim.
ag_news	The Federal Reserve raised interest rates by 25 basis points on Wednesday

Pretrained capabilities

Task	What it does	Classes	Test Acc	Test F1
sms_spam	SMS spam detection	2	99.1%	98.0%
dbpedia	Wikipedia article category	14	96.0%	95.9%
imdb	Movie review sentiment (long text)	2	94.8%	94.8%
banking77	Banking customer intent	77	94.3%	94.3%
ag_news	News topic (world/sports/business/tech)	4	90.5%	90.5%
sst2	Movie review sentiment	2	90.1%	90.1%
clinc_oos	Voice assistant intent + out-of-scope	151	88.4%	92.1%
massive_intent	Smart home voice commands	60	88.1%	86.4%
tweet_eval_offensive	Offensive language	2	81.0%	74.8%
tweet_eval_emotion	Tweet emotion	4	78.1%	74.7%
emotion	Text emotion (6 emotions)	6	75.5%	67.8%
tweet_eval_sentiment	Tweet sentiment (3-way)	3	66.2%	65.7%
snli	Natural language inference	3	65.6%	65.2%
doom_fire	Doom game-state decisions (fire/turn)	3	100.0%	100.0%
inbox_router	Email inbox routing (work/family/promo/notification)	4	70.8%	70.8%

Weaknesses: SNLI (65.6%) and tweet_eval_sentiment (66.2%) are below what task-specific models achieve. Emotion (75.5%) has limited class coverage. Probabilities are uncalibrated.

Train a custom classifier

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-train --example --save-dir my_models

Loaded 24 examples from reviews.csv
Training 'reviews': 24 examples, 2 classes
  Split: 19 train, 5 test
  Test accuracy: 100.0%
Saved to my_models/reviews/

--example uses a bundled 24-row product review CSV. To bring your own:

uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-train --input your_data.csv --text-col text --label-col label \
  --task-id your_task --save-dir my_models

Supports .csv, .tsv, and .jsonl.

Serve a custom model

JEFFY_PACK_DIR=my_models uvx --python 3.12 \
  --from "jeffy-classify @ git+https://github.com/nicobrenner/jeffy.git@v0.1.0-alpha.12" \
  jeffy-serve

curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "The battery life is amazing", "task": "reviews"}'
# → {"label": "positive", "confidence": 0.87, ...}

Install from source

git clone https://github.com/nicobrenner/jeffy.git
cd jeffy

# With uv (recommended)
uv venv && uv pip install -e .

# Or with pip
python -m venv .venv && source .venv/bin/activate
pip install -e .

The first prediction downloads the shared encoder (bge-large-en-v1.5, ~1.2 GB, cached afterward).

API

Python

from jeffy.engine import Engine

engine = Engine()
engine.load()

# List available classifiers
for name, cap in engine.capabilities.items():
    print(f"{name}: {cap.description} ({cap.n_classes} classes)")

# Classify text
result = engine.predict("banking77", "I was charged twice for the same transaction")
print(result["label"])          # "transaction_charged_twice"
print(result["confidence"])     # 0.999
print(result["probabilities"])  # {"transaction_charged_twice": 0.999, ...}

SDK walkthrough

HTTP

# Banking intent
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "I was charged twice for the same transaction", "task": "banking77"}'
# → {"label": "transaction_charged_twice", "confidence": 0.999, ...}

# Spam detection
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "WINNER! You have been selected for a free cruise. Reply YES to claim.", "task": "sms_spam"}'
# → {"label": "spam", "confidence": 0.91, ...}

# News topic
curl -s -X POST http://localhost:8400/v1/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "The Federal Reserve raised interest rates by 25 basis points on Wednesday", "task": "ag_news"}'
# → {"label": "Business", "confidence": 0.86, ...}

List capabilities

curl -s http://localhost:8400/v1/capabilities | python3 -c "
import json, sys
for c in json.load(sys.stdin)['capabilities']:
    print(f\"{c['task_id']:25s} {c['n_classes']:3d} classes  {c['test_accuracy']:.1%}  {c['name']}\")"

Per-capability metadata

Each shipped classifier has a manifest.json with label names, source dataset, HuggingFace path, stated license, encoder identity, training/test counts, and integrity hashes.

curl -s http://localhost:8400/v1/capabilities/banking77 | python3 -m json.tool

Train from Python

from jeffy.train import train_classifier

clf = train_classifier(
    texts=["great product!", "terrible service", "fast shipping", "broken on arrival"],
    labels=["positive", "negative", "positive", "negative"],
    task_id="my_reviews",
)

result = clf.predict("the quality exceeded my expectations")
print(result["label"])  # "positive"

clf.save("my_models")

Tuning options

Parameter	Default	Description
`C`	0.01	How aggressively the model fits your data. Low (0.001) = conservative, keeps predictions closer to "I'm not sure." High (1.0) = trusts individual training examples more. If the model is great on training data but bad on new data (overfitting), lower C.
`test_size`	0.2	What fraction of your data to hold back for testing. With 100 examples at 0.2, it trains on 80 and tests on 20. Set to 0 to train on everything (useful when you have very little data and will test manually).
`cv_folds`	3	Cross-validation: splits your training data into 3 parts, trains on 2 and tests on 1, rotates three times, averages the scores. Gives a more reliable accuracy estimate than a single split. Set to 0 to skip (faster, less reliable estimate).

Start with the defaults. With <50 examples per class, expect noisy estimates.

Reproduce the evaluation

# Install with build dependencies
uv pip install -e ".[build]"
# or: pip install -e ".[build]"

# Retrain all 13 heads from source datasets (~40 min, downloads ~5 GB)
jeffy-build --out data/model_pack

# Evaluate on held-out test sets with tuned baselines
jeffy-evaluate --baselines --latency --device cpu

Evaluation notes

SST-2: Evaluated on validation split (official test labels are not public).
SMS Spam: Random split (test_size=0.2, seed=42); no standard benchmark split.
SNLI: Input encoded as premise [SEP] hypothesis. Label -1 filtered.
CLINC-OOS: 151 classes including out-of-scope. In-scope accuracy 96.5%, OOS detection 51.7%.
MASSIVE: English only (config en).

Deployment

Component	Size	Required for
Jeffy package (wheel)	1.5 MB	Always (includes all 13 heads)
Encoder (bge-large-en-v1.5)	~1.2 GB	Inference (downloaded on first use)
`datasets` package	~100 MB	Retraining from HuggingFace only

Runtime memory: ~2 GB (encoder loaded once, shared across all heads).

Latency (CPU, single example, Linux aarch64):

Stage	p50	Notes
Embedding	50–80 ms	Dominates; varies with input length
Classifier	<1 ms	Negligible
Total	50–80 ms	End-to-end

Model security

Provenance and licensing

Dataset	Stated license
banking77, massive_intent, sms_spam	CC BY 4.0
clinc_oos	CC BY 3.0
dbpedia	CC BY-SA 3.0
snli	CC BY-SA 4.0
ag_news, imdb	Academic / non-commercial
sst2	Stanford academic license
emotion	Academic
tweet_eval_*	Twitter TOS / academic

The encoder (bge-large-en-v1.5) is MIT-licensed.

Tested

What's not included

No zero-shot / general classification. Each task needs a trained head. Unknown tasks return an error.
No LLM fallback. This release is pure embedding + classifier.
No automatic task routing. You must specify which classifier to use.
No hosted service. Runs locally only for now.

Roadmap

Status	Milestone
Available	Pretrained classifier library (14 text + 1 game-state), SDK/API, custom training from CSV/JSONL
Available	PyPI package (`pip install jeffy-classify`), non-text classifiers (numeric feature vectors)
Available	Classifier catalog UI with model detail pages, live try-it forms, and usage snippets
Available	Live Doom demo — real-time VizDoom classifier streaming in the playground
Available	Inbox classifier demo — animated training + classification workflow
Next	Live demos for more classifiers, broader catalog released in verified batches
Next	Landing page
Planned	Automatic routing among supported classifiers
Planned	Optional local/API LLM fallback for unsupported tasks
Planned	Hosted classifier catalog and decision-routing service
Exploring	Assisted labeling, retraining from corrections, classifier sharing

Suggestions for datasets, capabilities, or workflows are welcome as issues.

GitHub - nicobrenner/jeffy: Pretrained text classifiers you can run and retrain on CPU

Try it

Pretrained capabilities

Train a custom classifier

Serve a custom model

Install from source

API

Python

SDK walkthrough

HTTP

List capabilities

Per-capability metadata

Train from Python

Tuning options

Reproduce the evaluation

Evaluation notes

Deployment

Model security

Provenance and licensing

Tested

What's not included

Roadmap

Try it

Pretrained capabilities

Train a custom classifier

Serve a custom model

Install from source

API

Python

SDK walkthrough

HTTP

List capabilities

Per-capability metadata

Train from Python

Tuning options

Reproduce the evaluation

Evaluation notes

Deployment

Model security

Provenance and licensing

Tested

What's not included

Roadmap