metadata
license: mit
pipeline_tag: sentence-similarity
datasets:
- dell-research-harvard/headlines-semantic-similarity
- dell-research-harvard/AmericanStories
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
base_model: StoriesLM/StoriesLM-v1-1963
RepresentLM-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is trained on the HEADLINES semantic similarity dataset, using the StoriesLM-v1-1963 model as a base.
Usage
First install the sentence-transformers package:
pip install -U sentence-transformers
The model can then be used to encode language sequences:
from sentence_transformers import SentenceTransformer
sequences = ["This is an example sequence", "Each sequence is embedded"]
model = SentenceTransformer('RepresentLM/RepresentLM-v1')
embeddings = model.encode(sequences)
print(embeddings)