Model Details
Full Model IDXenova/mobilebert-uncased-mnli
Pipeline / Taskzero-shot-classification
Librarytransformers.js
Downloads (all-time)34.7K
Likes3
Last Modified7/11/2025
Author / OrgXenova
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("zero-shot-classification", model="Xenova/mobilebert-uncased-mnli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxmobileberttext-classificationzero-shot-classificationbase_model:typeform/mobilebert-uncased-mnlibase_model:quantized:typeform/mobilebert-uncased-mnliregion:us
More zero-shot-classification Models
See all →🚀 Use This Model
Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: zero-shot-classification
This model is designed for the zero-shot-classification task. Explore more models for this use case.
All zero-shot-classification Models →📊 Popularity
⬇ Downloads34.7K
❤️ Community Likes3
🛠️ Requirements
- →Install: pip install transformers.js
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.