flax-sentence-embeddings/st-codesearch-distilroberta-base
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.
Usage:
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 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)
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()
)