README.md

metadata

license: mit
language:
  - ru
base_model: cointegrated/rubert-tiny2
pipeline_tag: text-classification
tags:
  - rubert
  - bert
  - tiny
  - russian
  - classification
  - prompt-classification
  - intent-classification

rubert-tiny2-clf

A fine-tuned version of RuBERT-tiny2 for prompt intent classification on Russian text: given a user prompt, the model predicts which of three intents it corresponds to.

Label	Meaning
write	The prompt asks to write/generate text
draw	The prompt asks to draw/generate an image
neutral	Neither of the above

Usage

from transformers import pipeline

model = pipeline(model="r1char9/rubert-tiny2-clf")
model("Сгенерируй картину Томаса Шелби")
# [{'label': 'draw', 'score': 0.8699279427528381}]

Metrics

Evaluated on a held-out test set (support: 291 examples total).

Metric	write	draw	neutral	micro avg	macro avg	weighted avg
Precision	1.0	1.0	1.0	1.0	1.0	1.0
Recall	1.0	1.0	1.0	1.0	1.0	1.0
F1-score	1.0	1.0	1.0	1.0	1.0	1.0
Support	155	117	19	291	291	291
AUC-ROC	1.0	1.0	1.0	1.0	1.0	1.0

⚠️ All metrics at 1.0 across every class and averaging scheme usually signals evaluation on data overlapping with training data, a very small / easy test set, or a label-leakage issue — worth double-checking with a truly held-out, more diverse test set before relying on this number.

Limitations

• The neutral class has much lower support (19 examples) than write (155) and draw (117) — performance on the minority class may not be as reliable as the reported metrics suggest.
• The model is intended for short, single-intent prompts; behavior on long or multi-intent text is untested.