Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta
text-classification
Instructions to use cross-encoder/nli-deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-deberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-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-deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-base") - Notebooks
- Google Colab
- Kaggle
| epoch,steps,Accuracy | |
| 0,10000,0.8647301215851859 | |
| 0,20000,0.8722083736073664 | |
| 0,30000,0.8845195095894592 | |
| 0,40000,0.8869105153380475 | |
| 0,50000,0.8886910515338048 | |
| 0,-1,0.8922521239253193 | |
| 1,10000,0.8953553441522104 | |
| 1,20000,0.8956605789286259 | |
| 1,30000,0.898204202065422 | |
| 1,40000,0.9005443353512743 | |
| 1,50000,0.9018670193824083 | |
| 1,-1,0.9018670193824083 | |