anyclassifier 0.1.2

One Line To Build Any Classifier Without Data

∞🧙🏼‍♂️AnyClassifier - One Line To Build Any Classifier Without Data, And A Step Towards The First AI ML Engineer

Have you ever wanted/ been requested to build a classifier without any labeled data? What it takes now is just one line 🤯.

AnyClassifier is a framework that helps you build a classifier without any label, with as limited coding as possible.
As a machine learning engineer, one of the unavoidable but the most heavy lifting issues is to build and curate a high quality labelled data.
By leveraging LLM 🤖 annotation with permissive license, one can now label data at a better quality and at lightning speed ever.
This is inspired by some of the challenges I faced daily in work and doing open source - it is built for machine learning engineer and software engineer, by machine learning engineer 👨🏻‍💻.
By providing a higher level abstraction, this project's mission is to further democratizes AI to everyone, with ONE LINE.
The project is still experimental, but I found it worked in some of my use cases. Feedbacks welcome, and feel free to contribute. See Roadmap.
Together let's build more useful models.

🚀 Features

• One line to build any classifier that you don't have data 🤯
• Why one line? Not only it is easy to be used by Human but also it can easily be used by other LLM as a function call, easily to be integrated with any agentic flow
• Smoothness integration with transformers, setfit, fasttext and datasets
  • setfit: for limited data (e.g. 100) 🤗
  • fastText: for blazingly fast inference (1000 docs/s) without GPU ⚡️
  • transformers: for other usecase
• Huggingface-like interface for fastText that supports push_to_hub, saving and loading (let's not forget this amazing model before transformers architecture).

🏁 QuickStart in Apple Silicon - Train a model in 5 min!

# install (cp39 = python3.9, other valid values are cp310, cp311, cp312)
wget https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.84-metal/llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
pip install llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
rm llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
pip install anyclassifier
# run
cd examples
python train_setfit_model.py

🏁 QuickStart in Colab

Dataset	Colab Link
imdb sentiment classification

🔧 Installation

It is using llama.cpp as backend, and build wheel can take a lot of time (10min+), as such, we also provide an instruction to install with pre-built wheel.

Metal Backend (Apple's GPU) - cp39 = python3.9, other valid values are cp310, cp311, cp312

wget https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.84-metal/llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
pip install llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
rm llama_cpp_python-0.2.84-cp39-cp39-macosx_11_0_arm64.whl
pip install anyclassifier

Colab (T4) Prebuilt Wheel

wget https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.84-cu124/llama_cpp_python-0.2.84-cp310-cp310-linux_x86_64.whl
pip install llama_cpp_python-0.2.84-cp310-cp310-linux_x86_64.whl
rm llama_cpp_python-0.2.84-cp310-cp310-linux_x86_64.whl
pip install anyclassifier

CUDA Backend (Please read llama-cpp-python)

CMAKE_ARGS="-DGGML_METAL=on" pip install anyclassifier

CPU

CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" pip install anyclassifier

Developer's installation

pip install -e .

🛠️ Usage

Download a small LLM (please accept the respective terms and condition of model license beforehand)

meta-llama/Meta-Llama-3.1-8B-Instruct
google/gemma-2-9b

from huggingface_hub import hf_hub_download

# meta-llama/Meta-Llama-3.1-8B-Instruct
hf_hub_download("lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF", "Meta-Llama-3.1-8B-Instruct-Q8_0.gguf")

# google/gemma-2-9b
hf_hub_download("lmstudio-community/gemma-2-9b-it-GGUF", "gemma-2-9b-it-Q8_0.gguf")

One Liner

from huggingface_hub import hf_hub_download
from anyclassifier import build_anyclassifier
from anyclassifier.annotation.prompt import Label

unlabeled_dataset  # a huggingface datasets.Dataset class can be from your local json/ csv, or from huggingface hub.

# Magic One Line!
trainer = build_anyclassifier(
  "Classify a text's sentiment.",
  [
    Label(name='1', desc='positive sentiment'),
    Label(name='0', desc='negative sentiment')
  ],
  hf_hub_download("lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF", "Meta-Llama-3.1-8B-Instruct-Q8_0.gguf"),  # as you like
  unlabeled_dataset,
  column_mapping={"text": "text"},
  model_type="setfit",  # can be set to fastText
  push_dataset_to_hub=True,  # we recommend to push your dataset to huggingface, so that you won't lose it
  dataset_repo_id="user_id/test",
  is_dataset_private=True
)
# Share Your Model!
trainer.push_to_hub("user_id/any_model")

To Use Model

# SetFit
from setfit import SetFitModel

model = SetFitModel.from_pretrained("user_id/any_model")
preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
print(preds)

# FastText
from anyclassifier.fasttext_wrapper import FastTextForSequenceClassification

model = FastTextForSequenceClassification.from_pretrained("user_id/any_model")
preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
print(preds)

To Label a Dataset

from datasets import load_dataset
from anyclassifier.annotation.prompt import AnnotationPrompt, Label
from anyclassifier.annotation.annotator import LlamaCppAnnotator

unlabeled_dataset = load_dataset("somepath")
prompt = AnnotationPrompt(
  task_description="Classify a text's sentiment.",
  label_definition=[
    Label(name='1', desc='positive sentiment'),
    Label(name='0', desc='negative sentiment')
  ]
)
annotator = LlamaCppAnnotator(prompt)
label_dataset = annotator.annotate_dataset(unlabeled_dataset, n_record=1000)
label_dataset.push_to_hub('user_id/any_data')

See more examples:

model_type	example	resulting model	dataset
setfit	link	link	link
fasttext	link	link(probably need more label)	link

Test accuracy on imdb with SetFit: 90.42%

🗺️ Roadmap

• High Quality Data:
  • Prompt validation
  • Label validation - inter-model annotation
• High Quality Model
  • Auto error analysis
  • Auto model documentation
  • Auto synthetic data

👋 Contributing

• build models with AnyClassifier
• create issue/ PR
• suggest features

📩 Follow Me For Update:

X/ huggingface/ github