初始化项目，由ModelHub XC社区提供模型

Model: raphaelsty/semanlink_all_mpnet_base_v2
Source: Original Platform

README.md

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+---
+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)
+```