
## `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)
```
{"language": ["en", "fr"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch mpnet feature-extraction sentence-similarity en fr license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #fr #license-apache-2.0 #endpoints_compatible #region-us 


## '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 via the library 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:



Then you can use the model like this:


## '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 via the library 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:



Then you can use the model like this:
TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #fr #license-apache-2.0 #endpoints_compatible #region-us 
 ## '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 via the library 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:



Then you can use the model like this:
passage: TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #fr #license-apache-2.0 #endpoints_compatible #region-us 
## '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 via the library 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:



Then you can use the model like this: