Zero-Shot Classification
PyTorch
Transformers
sentence-transformers
English
zeroshot_classifier
bert
text-classification
Instructions to use claritylab/zero-shot-implicit-binary-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claritylab/zero-shot-implicit-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-implicit-binary-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/zero-shot-implicit-binary-bert") model = AutoModelForSequenceClassification.from_pretrained("claritylab/zero-shot-implicit-binary-bert") - sentence-transformers
How to use claritylab/zero-shot-implicit-binary-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("claritylab/zero-shot-implicit-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:
- ca450b5fe014533015de32da0491e8835a722c334dc6cd86a699db7f442c008a
- Size of remote file:
- 438 MB
- SHA256:
- b8d6d82f0cb731d8eeadb38b0e00b471e2dcebbea60b4bb58f9a40c3a7cbd645
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