Text Classification
Transformers
Safetensors
Russian
bert
sentiment-analysis
multi-label-classification
sentiment analysis
rubert
sentiment
tiny
russian
multilabel
classification
prompt-classification
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("r1char9/rubert-tiny2-clf")
model = AutoModelForSequenceClassification.from_pretrained("r1char9/rubert-tiny2-clf")Quick Links
Модель RuBERT-tiny2 была fine-tuned для задачи prompt classification, предназначенная для Russian текст. Выполняет задачу multi-label classification со слудующимим категориями:
0: write
1: draw
2: neutral
Категории для русского языка:
write: написать
draw: рисовать
neutral: нейтральность
Usage
from transformers import pipeline
model = pipeline(model="r1char9/rubert-tiny2-clf")
model('Сгенерируй картину Томаса Шелби')
# [{'label': 'draw', 'score': 0.8699279427528381}]
Metrics:
metrics 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 155.0 117.0 19.0 291.0 291.0 291.0
auc-roc 1.0 1.0 1.0 1.0 1.0 1.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="r1char9/rubert-tiny2-clf")