Model Details
Full Model IDcross-encoder/nli-MiniLM2-L6-H768
Pipeline / Taskzero-shot-classification
Librarysentence-transformers
Downloads (all-time)246.5K
Likes16
Last Modified4/15/2025
Author / Orgcross-encoder
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="cross-encoder/nli-MiniLM2-L6-H768")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
sentence-transformerspytorchonnxsafetensorsopenvinorobertatext-classificationtransformerszero-shot-classificationendataset:nyu-mll/multi_nlidataset:stanfordnlp/snlibase_model:nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Largebase_model:quantized:nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Largelicense:apache-2.0deploy:sagemakerdeploy:azureregion:us
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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
⬇ Downloads246.5K
❤️ Community Likes16
🛠️ Requirements
- →Install: pip install sentence-transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.