README.md

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)