Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
splade
sparse-encoder
code
custom_code
text-embeddings-inference
Instructions to use naver/splade-code-06B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-code-06B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/splade-code-06B", 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-06B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/splade-code-06B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("naver/splade-code-06B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("naver/splade-code-06B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Create generation_config.json
Browse files- generation_config.json +13 -0
generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.53.3"
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}
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