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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 338 Bytes
45a65f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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
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