dangvantuan tomaarsen HF staff commited on
Commit
1f111fd
1 Parent(s): af9724a

Add sentence-transformers library name (#14)

Browse files

- Add sentence-transformers library name (308a96c64983cbd28ce46349babd2cba427e0338)


Co-authored-by: Tom Aarsen <[email protected]>

Files changed (1) hide show
  1. README.md +5 -4
README.md CHANGED
@@ -12,7 +12,7 @@ license: apache-2.0
12
  model-index:
13
  - name: sentence-camembert-large by Van Tuan DANG
14
  results:
15
- - task:
16
  name: Sentence-Embedding
17
  type: Text Similarity
18
  dataset:
@@ -20,9 +20,10 @@ model-index:
20
  type: stsb_multi_mt
21
  args: fr
22
  metrics:
23
- - name: Test Pearson correlation coefficient
24
- type: Pearson_correlation_coefficient
25
- value: xx.xx
 
26
  ---
27
  ## Description:
28
  [**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the 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 in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in queries and documents, offering a powerful semantic search.
 
12
  model-index:
13
  - name: sentence-camembert-large by Van Tuan DANG
14
  results:
15
+ - task:
16
  name: Sentence-Embedding
17
  type: Text Similarity
18
  dataset:
 
20
  type: stsb_multi_mt
21
  args: fr
22
  metrics:
23
+ - name: Test Pearson correlation coefficient
24
+ type: Pearson_correlation_coefficient
25
+ value: xx.xx
26
+ library_name: sentence-transformers
27
  ---
28
  ## Description:
29
  [**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the 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 in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in queries and documents, offering a powerful semantic search.