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Model: flax-sentence-embeddings/st-codesearch-distilroberta-base
Source: Original Platform

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+datasets:
+- code_search_net
+---
+
+# 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.
+
+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)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('flax-sentence-embeddings/st-codesearch-distilroberta-base')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+## Training
+
+The model was trained with a DistilRoBERTa-base model for 10k training steps on the codesearch dataset with batch_size 256 and MultipleNegativesRankingLoss. 
+
+It is some preliminary model. It was neither tested nor was the trained quite sophisticated 
+
+
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`MultiDatasetDataLoader.MultiDatasetDataLoader` of length 5371 with parameters:
+```
+{'batch_size': 256}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20, 'similarity_fct': 'dot_score'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "callback": null,
+    "epochs": 1,
+    "evaluation_steps": 0,
+    "evaluator": "NoneType",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "warmupconstant",
+    "steps_per_epoch": 10000,
+    "warmup_steps": 500,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Normalize()
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->