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julep-ai
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dfe-base-en

Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
text-embeddings-inference
Inference Endpoints
Model card Files Community
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julep commited on
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1 Parent(s): c9f5a1b

feat: Final model

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Signed-off-by: Julep Developers <[email protected]>

Files changed (8) hide show
  1. 2_Asym/140654129191936_Dense/pytorch_model.bin +1 -1
  2. 2_Asym/140654129198896_Dense/pytorch_model.bin +1 -1
  3. 2_Asym/140654129199328_Dense/pytorch_model.bin +1 -1
  4. 2_Asym/140654129199376_Dense/pytorch_model.bin +1 -1
  5. 2_Asym/140654129388880_Dense/pytorch_model.bin +1 -1
  6. 2_Asym/140663133264320_Dense/pytorch_model.bin +1 -1
  7. README.md +2 -3
  8. eval/triplet_evaluation_results.csv +2 -0
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README.md CHANGED
@@ -1,5 +1,4 @@
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  ---
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- license: mit
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  pipeline_tag: sentence-similarity
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  tags:
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  - sentence-transformers
@@ -8,7 +7,7 @@ tags:
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  ---
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- # dfe-base-en
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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@@ -28,7 +27,7 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('julep-ai/dfe-base-en')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  ---
 
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  pipeline_tag: sentence-similarity
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  tags:
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  - sentence-transformers
 
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  ---
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+ # {MODEL_NAME}
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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+ model = SentenceTransformer('{MODEL_NAME}')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
eval/triplet_evaluation_results.csv CHANGED
@@ -9,3 +9,5 @@ epoch,steps,accuracy_cosinus,accuracy_manhattan,accuracy_euclidean
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