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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 337 Bytes
ec8d4ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | epoch,steps,Accuracy
0,10000,0.8567649378068324
0,20000,0.8696614351486786
0,30000,0.8731971612443721
0,40000,0.8798107496248061
0,50000,0.880522982219622
0,-1,0.886246279856536
1,10000,0.8877216188029405
1,20000,0.8890952102357998
1,30000,0.8895276371683667
1,40000,0.8935212270750134
1,50000,0.8950728766565768
1,-1,0.8953272454404395
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