tomaarsen HF staff commited on
Commit
8a5de30
1 Parent(s): bb16507

Add sentence-transformers library name

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Hello!

## Pull Request overview
* Add sentence-transformers library name

## Details
This allows this model to be freely available over Inference API and in the Widget like this:

![image.png](https://cdn-uploads.huggingface.co/production/uploads/6317233cc92fd6fee317e030/3XsPwz4tN_TTJh-t1wvgy.png)

- Tom Aarsen

Files changed (1) hide show
  1. README.md +5 -4
README.md CHANGED
@@ -12,7 +12,7 @@ license: apache-2.0
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  model-index:
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  - name: sentence-camembert-large by Van Tuan DANG
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  results:
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- - task:
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  name: Sentence-Embedding
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  type: Text Similarity
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  dataset:
@@ -20,9 +20,10 @@ model-index:
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  type: stsb_multi_mt
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  args: fr
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  metrics:
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- - name: Test Pearson correlation coefficient
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- type: Pearson_correlation_coefficient
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- value: 88.63
 
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  ---
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  ## Description:
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  This [**Sentence-CamemBERT-Large**](https://huggingface.co/Lajavaness/sentence-camembert-large) Model is an Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence as a mathematical vector, allowing it to understand the meaning of the text beyond individual words in queries and documents. It offers powerful semantic search capabilities.
 
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  model-index:
13
  - name: sentence-camembert-large by Van Tuan DANG
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  results:
15
+ - task:
16
  name: Sentence-Embedding
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  type: Text Similarity
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  dataset:
 
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  type: stsb_multi_mt
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  args: fr
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  metrics:
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+ - name: Test Pearson correlation coefficient
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+ type: Pearson_correlation_coefficient
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+ value: 88.63
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+ library_name: sentence-transformers
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  ---
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  ## Description:
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  This [**Sentence-CamemBERT-Large**](https://huggingface.co/Lajavaness/sentence-camembert-large) Model is an Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence as a mathematical vector, allowing it to understand the meaning of the text beyond individual words in queries and documents. It offers powerful semantic search capabilities.