Sentence Similarity
sentence-transformers
Safetensors
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use algoprog/ANCE-distilbert-ProCIS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use algoprog/ANCE-distilbert-ProCIS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("algoprog/ANCE-distilbert-ProCIS") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use algoprog/ANCE-distilbert-ProCIS with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("algoprog/ANCE-distilbert-ProCIS") model = AutoModel.from_pretrained("algoprog/ANCE-distilbert-ProCIS") - Notebooks
- Google Colab
- Kaggle
ANCE ProCIS
This is a sentence-transformers model based on DistilBERT: It maps conversations and passages to dense embeddings for retrieval in the context of conversations. It's trained on the ProCIS dataset.
Citing & Authors
@article{Anonymous_2024_ProCIS,
title = {ProCIS: A Benchmark for Proactive Retrieval in Conversations},
author = {},
journal = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year = {2024}
}
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