---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---
# Kwaipilot OASIS-1.3B
## Model Details
**Model Name**: OASIS (Optimized Augmentation Strategy for Improved code Search)
**Introduction**
OASIS is a state-of-the-art code embedding model developed by Kwaipilot. This model incorporates unique, proprietary methods including **repository-level program analysis**, the **OASIS-instruct data synthesis** algorithm, and a **specialized fusion loss function**, setting new benchmarks in code search efficiency and accuracy.
**Intended Use**
This model is ideal for developers and researchers engaged in enhancing **code retrieval systems**. OASIS excels in scenarios requiring semantic understanding and retrieval of code snippets within varied programming contexts.
**Training and Performance**
OASIS was trained on a synthetic dataset created through repository-level analysis, ensuring broad understanding across different coding styles and languages. It has demonstrated state-of-the-art performance on latest code search benchmarks.
## Future Directions
Kwaipilot upcoming initiatives include:
- Open sourcing improved models.
- Releasing technical reports.
- Releasing natural language processing models.
- ...
## Performance
| | Size | CoSQA | AdvTest | CSN-Py | CSN-Ja | CSN-JS | CSN-PHP | CSN-Go | CSN-Ruby |
|-----------------|:-----:|:------:|:---------:|:--------:|:-------:|:-------:|:-------:|:-------:|:-------:|
|Openai-Embedding-Ada-002 | Unknown | 0.4423| 0.3808 | 0.6802 | 0.7149| 0.6750| 0.6062| 0.8563| 0.7472|
|jina-embeddings-v2-base-code | 161M |0.6837 |0.385 | 0.6634 | 0.6803| 0.6304| 0.5701| 0.8595| 0.7095|
| CodeSage-large | 1.3B | 0.4753| 0.5267 | 0.7077 | 0.7021| 0.695 | 0.6133| 0.8371| 0.7192|
| CodeFuse-CGE-Small | 3.8B | 0.5619| 0.4639 | 0.6958 | 0.6863| 0.6564| 0.6133| 0.8637| 0.7341|
| OASIS-1.3B | 1.3B | 0.5532| 0.4861 | 0.701 | 0.7199| 0.6727| 0.6217| 0.8732| 0.7333|
## Usage
### Direct Usage
```bash
pip install -U torch
pip install -U transformers
```
Avoid using torch=2.5.0 when loading the model with torch_dtype=torch.bfloat16. For optimal performance and stability, please use PyTorch version 2.4.1 or earlier, or upgrade to 2.5.1 or later.
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoModel, AutoTokenizer
def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
# Add query prompt
def get_query_prompt(query: str):
query_description = 'Given a code search query, retrieve relevant code snippet that answer the query'
prompt = f'Instruct: {query_description}\nQuery: {query}'
return prompt
query = "How to do quicksort in python?"
code1 = """def bubble_sort(arr):
n = len(arr)
for i in range(n):
swapped = False
for j in range(1, n - i):
if arr[j - 1] > arr[j]:
arr[j - 1], arr[j] = arr[j], arr[j - 1]
swapped = True
if not swapped:
break
return arr"""
code2 = """def quick_sort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[0]
less = [x for x in arr[1:] if x <= pivot]
greater = [x for x in arr[1:] if x > pivot]
return quick_sort(less) + [pivot] + quick_sort(greater)"""
model = AutoModel.from_pretrained("Kwaipilot/OASIS-code-1.3B", output_hidden_states=True)
tokenizer = AutoTokenizer.from_pretrained("Kwaipilot/OASIS-code-1.3B")
# Tokenize and inference
inputs = tokenizer([get_query_prompt(query), code1, code2], max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**inputs)
# Last token pooling
embeddings = last_token_pool(outputs.hidden_states[-1], inputs['attention_mask'])
print(embeddings.shape)
# torch.Size([3, 2048])
embeddings = F.normalize(embeddings, dim=1, p=2)
similarity = embeddings @ embeddings.T
print(similarity[0, 1:])
# tensor([0.6495, 0.8036])
```
### Sentence Transformers
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Kwaipilot/OASIS-code-1.3B")#, model_kwargs={"torch_dtype": torch.bfloat16})
query = "How to do quicksort in python?"
code1 = """def bubble_sort(arr):
n = len(arr)
for i in range(n):
swapped = False
for j in range(1, n - i):
if arr[j - 1] > arr[j]:
arr[j - 1], arr[j] = arr[j], arr[j - 1]
swapped = True
if not swapped:
break
return arr"""
code2 = """def quick_sort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[0]
less = [x for x in arr[1:] if x <= pivot]
greater = [x for x in arr[1:] if x > pivot]
return quick_sort(less) + [pivot] + quick_sort(greater)"""
# Run inference
query_embedding = model.encode([query], prompt_name="query")
code_embeddings = model.encode([code1, code2])
print(code_embeddings.shape)
# (2, 2048)
# Get the similarity scores for the embeddings
print(model.similarity(query_embedding[0], code_embeddings[0]))
print(model.similarity(query_embedding[0], code_embeddings[1]))
# tensor([[0.6495]])
# tensor([[0.8036]])
```
### BibTeX
```bibtex
@misc{kwaipilotoasis,
title = {Optimized Augmentation Strategy for Improved code Search},
author = {Kwaipilot team},
year = {2024},
}
```