| --- |
| 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: |
|
|
| ```bash |
| 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("suproteem/RepresentLM-v2") |
| embeddings = model.encode(sequences) |
| print(embeddings) |
| ``` |
|
|
