This is a sentence-transformers 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 dataset and can be used to search program code given text.


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']))

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

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('flax-sentence-embeddings/st-codesearch-distilroberta-base')
embeddings = model.encode(sentences)


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:


MultiDatasetDataLoader.MultiDatasetDataLoader of length 5371 with parameters:

{'batch_size': 256}


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

  (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()

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