Zero-Shot Classification
PyTorch
Transformers
sentence-transformers
English
zeroshot_classifier
bert
text-classification
Instructions to use claritylab/zero-shot-explicit-binary-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claritylab/zero-shot-explicit-binary-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="claritylab/zero-shot-explicit-binary-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/zero-shot-explicit-binary-bert") model = AutoModelForSequenceClassification.from_pretrained("claritylab/zero-shot-explicit-binary-bert") - sentence-transformers
How to use claritylab/zero-shot-explicit-binary-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("claritylab/zero-shot-explicit-binary-bert") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15b88a1fb796d56a4a7e289408348d74bce6f47f06a3c7a899214da4c901db89
- Size of remote file:
- 438 MB
- SHA256:
- 3d599fdcf7a63c4ef4903a3539c700d44e29c2140c8269d5854741361c65430d
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