Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
Instructions to use cross-encoder/nli-MiniLM2-L6-H768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-MiniLM2-L6-H768 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-MiniLM2-L6-H768") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cross-encoder/nli-MiniLM2-L6-H768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-MiniLM2-L6-H768")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-MiniLM2-L6-H768") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-MiniLM2-L6-H768") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdc9cb6ea674bf05dcb48b7918c0cfe2eff7454af9d84379085db6ca0e768fd0
- Size of remote file:
- 329 MB
- SHA256:
- 768059960825f2365e301878e8cbe816620f04546769e38063e4d21f297dc48a
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