Text Classification
sentence-transformers
PyTorch
Transformers
xlm-roberta
text-embeddings-inference
Instructions to use nitsuai/bce-reranker-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nitsuai/bce-reranker-base_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nitsuai/bce-reranker-base_v1") 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 nitsuai/bce-reranker-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nitsuai/bce-reranker-base_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nitsuai/bce-reranker-base_v1") model = AutoModelForSequenceClassification.from_pretrained("nitsuai/bce-reranker-base_v1") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2 opened 12 months ago
by
SFconvertbot
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
#1 opened about 1 year ago
by
tomaarsen