Zero-Shot Classification
Transformers
PyTorch
Safetensors
Russian
bert
text-classification
rubert
russian
nli
rte
Instructions to use cointegrated/rubert-tiny-bilingual-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny-bilingual-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cointegrated/rubert-tiny-bilingual-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny-bilingual-nli") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-tiny-bilingual-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
metadata
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: Сервис отстойный, кормили невкусно
candidate_labels: Мне понравилось, Мне не понравилось
hypothesis_template: '{}.'
datasets:
- cointegrated/nli-rus-translated-v2021
RuBERT-tiny for NLI (natural language inference)
This is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a related model: https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway