Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta-v2
text-classification
Instructions to use cross-encoder/nli-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-deberta-v3-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-v3-large") 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-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-v3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-v3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| epoch,steps,Accuracy | |
| 0,10000,0.9075138627460956 | |
| 0,20000,0.8990690339319326 | |
| 0,30000,0.9016635295314647 | |
| 0,40000,0.900137355649387 | |
| 0,50000,0.9045632599074122 | |
| 0,-1,0.9029862135625986 | |
| 1,10000,0.9057841990130743 | |
| 1,20000,0.9093961438673246 | |
| 1,30000,0.906547285954113 | |
| 1,40000,0.9111766800630818 | |
| 1,50000,0.9114310423767614 | |
| 1,-1,0.9131098336470469 | |
| 2,10000,0.9129572162588391 | |
| 2,20000,0.9115836597649692 | |
| 2,30000,0.9147377524545963 | |
| 2,40000,0.9150429872310119 | |
| 2,50000,0.9145342626036527 | |
| 2,-1,0.913720303199878 | |
| 3,10000,0.9127028539451595 | |
| 3,20000,0.9131607061097827 | |
| 3,30000,0.9142799002899731 | |
| 3,40000,0.916213053873938 | |
| 3,50000,0.9167726509640332 | |
| 3,-1,0.9172305031286565 | |