Zero-Shot Classification
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
Instructions to use cross-encoder/nli-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-distilroberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-distilroberta-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-distilroberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-distilroberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-distilroberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-distilroberta-base") - Notebooks
- Google Colab
- Kaggle
File size: 337 Bytes
3616151 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | epoch,steps,Accuracy
0,10000,0.8128608857121055
0,20000,0.8258336936891105
0,30000,0.8371785414493933
0,40000,0.849668048737059
0,50000,0.8555439676442906
0,-1,0.854857171927861
1,10000,0.8612418284028184
1,20000,0.8619540609976344
1,30000,0.8658459033907359
1,40000,0.8682115330806603
1,50000,0.8688474550403175
1,-1,0.8696614351486786
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