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
Update config.json
Browse files- config.json +5 -1
config.json
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"padding_side": "left",
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"torch_dtype": "bfloat16",
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"train_head": false,
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"transformers_version": "4.53.3"
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}
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"padding_side": "left",
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"torch_dtype": "bfloat16",
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"train_head": false,
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"transformers_version": "4.53.3",
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"auto_map": {
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"AutoConfig": "splade.SpladeConfig",
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"AutoModelForCausalLM": "splade.Splade"
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}
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}
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