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
| 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 | |