---
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](https://www.SBERT.net) 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](https://huggingface.co/datasets/dell-research-harvard/headlines-semantic-similarity) semantic similarity dataset, using the [StoriesLM-v1-1963](https://huggingface.co/StoriesLM/StoriesLM-v1-1963) model as a base.

## Usage

First install the [sentence-transformers](https://www.SBERT.net) package:

```
pip install -U sentence-transformers
```

The model can then be used to encode language sequences:

```python
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)
```