Text Classification
sentence-transformers
PyTorch
Transformers
xlm-roberta
text-embeddings-inference
Instructions to use maidalun1020/bce-reranker-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use maidalun1020/bce-reranker-base_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("maidalun1020/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 maidalun1020/bce-reranker-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maidalun1020/bce-reranker-base_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maidalun1020/bce-reranker-base_v1") model = AutoModelForSequenceClassification.from_pretrained("maidalun1020/bce-reranker-base_v1") - Inference
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- bc6fa43b0dc2c15fbd2020cd374a00be08c0029102de0ac91a9317f752e737a2
- Size of remote file:
- 2.02 MB
- SHA256:
- 0b5068a460fe2a354ec04585650886f2ce6a868e04bab663190e4b861079448d
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