GeRaCl: General Rapid text Classifier

GeRaCl is an open‑source framework for building, training, and evaluating efficient zero‑shot text classifiers on top of any BERT‑like sentence-encoder. It is inspired by the GLiNER framework.

✨ Why GeRaCl?

Feature	What it means for you
Zero‑shot by design	Classify with arbitrary label sets that you decide at run‑time — just pass a list of strings.
One forward pass	As fast as ordinary text classification; no pairwise loops like in NLI‑based approaches.
Model‑agnostic	Works with any Hugging Face sentence-encoder.
155 M reference checkpoint	A lean baseline (155M parameters) that beats much larger sentence‑encoders (300-500M parameters).
All‑in‑one toolkit	Training/eval scripts, HF Hub and WandB integration.

🚀 Quick Start

Clone and install directly from GitHub:

git clone https://github.com/deepvk/geracl
cd geracl

pip install -r requirements.txt

Verify your installation:

import geracl
print(geracl.__version__)

🧑‍💻 Usage Examples

Single classification scenario

from transformers import AutoTokenizer
from geracl import GeraclHF, ZeroShotClassificationPipeline

model = GeraclHF.from_pretrained('deepvk/GeRaCl-USER2-base').to('cuda').eval()
tokenizer  = AutoTokenizer.from_pretrained('deepvk/GeRaCl-USER2-base')

pipe = ZeroShotClassificationPipeline(model, tokenizer, device="cuda")

text = "Утилизация катализаторов: как неплохо заработать"
labels = ["экономика", "происшествия", "политика", "культура", "наука", "спорт"]
result = pipe(text, labels, batch_size=1)[0]

print(labels[result])

Multiple classification scenarios

from transformers import AutoTokenizer
from geracl import GeraclHF, ZeroShotClassificationPipeline

model = GeraclHF.from_pretrained('deepvk/GeRaCl-USER2-base').to('cuda').eval()
tokenizer  = AutoTokenizer.from_pretrained('deepvk/GeRaCl-USER2-base')

pipe = ZeroShotClassificationPipeline(model, tokenizer, device="cuda")

texts = [
  "Утилизация катализаторов: как неплохо заработать",
  "Мне не понравился этот фильм."
]
labels = [
  ["экономика", "происшествия", "политика", "культура", "наука", "спорт"],
  ["нейтральный", "позитивный", "негативный"]
]
results = pipe(texts, labels, batch_size=2)

for i in range(len(labels)):
    print(labels[i][results[i]])