Zero-Shot Classification
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
Instructions to use cross-encoder/nli-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-roberta-base") 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-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-roberta-base") - Notebooks
- Google Colab
- Kaggle
nreimers commited on
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README.md
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language: en
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tags:
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- roberta-base
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datasets:
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- snli
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metrics:
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- accuracy
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# Cross-Encoder for Natural Language Inference
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language: en
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pipeline_tag: zero-shot-classification
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tags:
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- roberta-base
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datasets:
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- snli
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metrics:
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- accuracy
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# Cross-Encoder for Natural Language Inference
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