metadata
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- semantic-similarity
- representlm
base_model: suproteem/StoriesLM-v2-1979
RepresentLM-v2
This is a sentence-transformers model: It maps sentences and 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-v2-1979 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("suproteem/RepresentLM-v2")
embeddings = model.encode(sequences)
print(embeddings)