Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
splade
sparse-encoder
code
custom_code
text-embeddings-inference
Instructions to use naver/splade-code-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-code-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/splade-code-8B", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use naver/splade-code-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/splade-code-8B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("naver/splade-code-8B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("naver/splade-code-8B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-sa-4.0 | |
| tags: | |
| - sentence-transformers | |
| - transformers | |
| - splade | |
| - sparse-encoder | |
| - code | |
| pipeline_tag: feature-extraction | |
| SPLADE-Code-8B is a sparse retrieval model designed for code retrieval tasks. | |
| ## Usage | |
| ### Using Sentence Transformers | |
| Install Sentence Transformers: | |
| ```bash | |
| pip install sentence_transformers | |
| ``` | |
| ```python | |
| from sentence_transformers import SparseEncoder | |
| model = SparseEncoder("naver/splade-code-8B", trust_remote_code=True) | |
| queries = [ | |
| "SELECT *\nFROM Student\nWHERE Age = (\nSELECT MAX(Age)\nFROM Student\nWHERE Group = 'specific_group'\n)\nAND Group = 'specific_group';" | |
| ] | |
| query_embeddings = model.encode(queries) | |
| print(query_embeddings.shape) | |
| # torch.Size([1, 151936]) | |
| sparsity = model.sparsity(query_embeddings) | |
| print(sparsity) | |
| # {'active_dims': 1120.0, 'sparsity_ratio': 0.9926284751474305} | |
| decoded = model.decode(query_embeddings, top_k=10) | |
| print(decoded) | |
| # [[ | |
| # ('Δ group', 2.34375), | |
| # ('Δ oldest', 2.28125), | |
| # ('Δ age', 2.25), | |
| # ('_group', 2.25), | |
| # ('Δ Group', 2.171875), | |
| # ('Δ Age', 2.109375), | |
| # ('Δ MAX', 2.0625), | |
| # ('Δ Student', 2.046875), | |
| # ('Δ specific', 2.03125), | |
| # ('Δ student', 2.0), | |
| # ]] | |
| ``` | |
| ### Using Transformers | |
| ```bash | |
| pip install transformers | |
| ``` | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoModel | |
| import os | |
| import torch | |
| splade = AutoModelForCausalLM.from_pretrained("naver/splade-code-8B", trust_remote_code=True) | |
| device = (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")) | |
| splade.to(device) | |
| splade.eval() | |
| queries = ["SELECT *\nFROM Student\nWHERE Age = (\nSELECT MAX(Age)\nFROM Student\nWHERE Group = 'specific_group'\n)\nAND Group = 'specific_group';"] | |
| bow_dict = splade.encode(queries, prompt_type="query", top_k_q=10, return_dict=True, print_dict=True) | |
| ``` | |
| ``` | |
| +--------------------------------------------------------------------+ | |
| | TOP ACTIVATED WORDS | | |
| +--------------------------------------------------------------------+ | |
| * INPUT: SELECT * | |
| FROM Student | |
| WHERE Age = ( | |
| SELECT MAX(Age) | |
| FROM Student | |
| WHERE Group = 'specific_group' | |
| ) | |
| AND Group = 'specific_group'; | |
| Δ group | ββββββββββββββββββββ 2.34 | |
| Δ oldest | βββββββββββββββββββ 2.28 | |
| Δ age | βββββββββββββββββββ 2.25 | |
| _group | βββββββββββββββββββ 2.25 | |
| Δ Group | ββββββββββββββββββ 2.17 | |
| Δ Age | ββββββββββββββββββ 2.11 | |
| Δ MAX | βββββββββββββββββ 2.06 | |
| Δ Student | βββββββββββββββββ 2.05 | |
| Δ specific | βββββββββββββββββ 2.03 | |
| Δ student | βββββββββββββββββ 2.00 | |
| ``` | |