Instructions to use hamzaMM/questionClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzaMM/questionClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hamzaMM/questionClassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hamzaMM/questionClassifier") model = AutoModelForSequenceClassification.from_pretrained("hamzaMM/questionClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 241 Bytes
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```python
from transformers import pipeline
pipe = pipeline(model="hamzaMM/questionClassifier", task="text-classification")
def is_question(input: str):
return pipe(input)[0]['label'] == 'LABEL_1'
is_question("How is the weather")
``` |