| | --- |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | language: |
| | - en |
| | - fr |
| | license: apache-2.0 |
| | --- |
| | |
| | ## `semanlink_all_mpnet_base_v2` |
| |
|
| | 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. |
| |
|
| | `semanlink_all_mpnet_base_v2` has been fine-tuned on the knowledge graph [Semanlink](http://www.semanlink.net/sl/home?lang=fr) via the library [MKB](https://github.com/raphaelsty/mkb) on the link-prediction task. The model is dedicated to the representation of both technical and generic terminology in machine learning, NLP, news. |
| |
|
| | ## Usage (Sentence-Transformers) |
| |
|
| | Using this model becomes easy when you have sentence-transformers installed: |
| |
|
| | ``` |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can use the model like this: |
| |
|
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | sentences = ["Machine Learning", "Geoffrey Hinton"] |
| | |
| | model = SentenceTransformer('raphaelsty/semanlink_all_mpnet_base_v2') |
| | embeddings = model.encode(sentences) |
| | print(embeddings) |
| | ``` |
