Updates README.md

README.md

@@ -5,14 +5,13 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+- embeddings
+license: mit
 ---
 
 # {MODEL_NAME}
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
+This is a fine-tuned embedding model of [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It was fine-tuned on a dataset based on an ISO 27001 text corpus consisting of text chunks (1024 characters) and associated questions. A total of 2.000 chunk and question pairs were generated. The fine-tuning process is specialized on an Information Retrieval task in which the generated questions are used to find the relevant chunks. The effectiveness of the model is evaluated on whether the correct chunk was retrieved, and the loss is calculated with the multiple negative ranking loss.
 
 ## Usage (Sentence-Transformers)
 
@@ -33,15 +32,6 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -88,5 +78,4 @@ SentenceTransformer(
 ```
 
 ## Citing & Authors
-
-<!--- Describe where people can find more information -->
+Based on https://huggingface.co/BAAI/bge-large-en-v1.5 from Xiao et al. (2023) (C-Pack: Packaged Resources To Advance General Chinese Embedding)