Instructions to use I77/question_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use I77/question_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="I77/question_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("I77/question_classifier") model = AutoModelForSequenceClassification.from_pretrained("I77/question_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 660 Bytes
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tags:
- text-classification
library_name: transformers
datasets:
- I77/valid_invalid_questions
language:
- ru
metrics:
- accuracy
- f1
base_model:
- DeepPavlov/rubert-base-cased
---
# RuBERT Model for Logical Question Classification
This model classifies questions as logically correct or incorrect.
1 - logically correct
0 - logically incorrect
# Metrics on validation
Accuracy: 0.9500
Precision: 1.0000
Recall: 0.9000
F1-score: 0.9474
### Example inference
```python
from transformers import pipeline
classifier = pipeline('text-classification', model='I77/question_classifier')
print(classifier('Этот вопрос логичен?'))
``` |