Update README.md

README.md

@@ -4,13 +4,59 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
+datasets:
+- code_search_net
 ---
 
-# {MODEL_NAME}
+# flax-sentence-embeddings/st-codesearch-distilroberta-base
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+It was trained on the [code_search_net](https://huggingface.co/datasets/code_search_net) dataset and can be used to search program code given text.
+
+## Usage:
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+
+#This list the defines the different programm codes
+code = ["""def sort_list(x):
+   return sorted(x)""",
+"""def count_above_threshold(elements, threshold=0):
+    counter = 0
+    for e in elements:
+        if e > threshold:
+            counter += 1
+    return counter""",
+"""def find_min_max(elements):
+    min_ele = 99999
+    max_ele = -99999
+    for e in elements:
+        if e < min_ele:
+            min_ele = e
+        if e > max_ele:
+            max_ele = e
+    return min_ele, max_ele"""]
+    
+
+model = SentenceTransformer("flax-sentence-embeddings/st-codesearch-distilroberta-base")
+
+# Encode our code into the vector space
+code_emb = model.encode(code, convert_to_tensor=True)
+
+# Interactive demo: Enter queries, and the method returns the best function from the 
+# 3 functions we defined
+while True:
+    query = input("Query: ")
+    query_emb = model.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(query_emb, code_emb)[0]
+    top_hit = hits[0]
+
+    print("Cossim: {:.2f}".format(top_hit['score']))
+    print(code[top_hit['corpus_id']])
+    print("\n\n")
+```
 
 ## Usage (Sentence-Transformers)
 
@@ -26,28 +72,26 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('flax-sentence-embeddings/st-codesearch-distilroberta-base')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
 
+## Training
 
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
+The model was trained with a DistilRoBERTa-base model for 10k training steps on the codesearch dataset with batch_size 256 and MultipleNegativesRankingLoss. 
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+It is some preliminary model. It was neither tested nor was the trained quite sophisticated 
 
 
-## Training
 The model was trained with the parameters:
 
 **DataLoader**:
 
 `MultiDatasetDataLoader.MultiDatasetDataLoader` of length 5371 with parameters:
 ```
-{'batch_size': 'unknown'}
+{'batch_size': 256}
 ```
 
 **Loss**: