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
CHANGED
|
@@ -57,5 +57,9 @@
|
|
| 57 |
"transformers_version": "4.53.3",
|
| 58 |
"use_cache": true,
|
| 59 |
"use_sliding_window": false,
|
| 60 |
-
"vocab_size": 151936
|
|
|
|
|
|
|
|
|
|
|
|
|
| 61 |
}
|
|
|
|
| 57 |
"transformers_version": "4.53.3",
|
| 58 |
"use_cache": true,
|
| 59 |
"use_sliding_window": false,
|
| 60 |
+
"vocab_size": 151936,
|
| 61 |
+
"auto_map": {
|
| 62 |
+
"AutoConfig": "splade.SpladeConfig",
|
| 63 |
+
"AutoModelForCausalLM": "splade.Splade"
|
| 64 |
+
}
|
| 65 |
}
|