Feature Extraction
sentence-transformers
Safetensors
Transformers
mteb
custom_code
Eval Results (legacy)
Instructions to use OrcaDB/cde-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OrcaDB/cde-small-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OrcaDB/cde-small-v1", 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 OrcaDB/cde-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OrcaDB/cde-small-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OrcaDB/cde-small-v1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add new SentenceTransformer model.
Browse files- config.json +3 -3
- config_sentence_transformers.json +3 -3
config.json
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@@ -1,12 +1,12 @@
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{
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"_name_or_path": "
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"architecture": "transductive",
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"architectures": [
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"DatasetTransformer"
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],
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"attn_implementation": null,
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"auto_map": {
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"AutoConfig": "
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"AutoModel": "jxm/cde-small-v1--model.DatasetTransformer"
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},
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"biencoder_pooling_strategy": "mean",
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"transductive_sequence_dropout_prob": 0.0,
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"transductive_tie_token_embeddings": false,
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"transductive_tokens_per_document": 1,
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"transformers_version": "4.
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}
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{
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"_name_or_path": "./temp/cde-small",
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"architecture": "transductive",
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"architectures": [
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"DatasetTransformer"
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],
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"attn_implementation": null,
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"auto_map": {
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+
"AutoConfig": "misc.ContextualModelConfig",
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"AutoModel": "jxm/cde-small-v1--model.DatasetTransformer"
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},
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"biencoder_pooling_strategy": "mean",
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"transductive_sequence_dropout_prob": 0.0,
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"transductive_tie_token_embeddings": false,
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"transductive_tokens_per_document": 1,
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"transformers_version": "4.45.2"
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "3.
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"transformers": "4.
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"pytorch": "2.5.
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},
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"prompts": {
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"query": "search_query: ",
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{
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"__version__": {
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"sentence_transformers": "3.1.1",
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"transformers": "4.45.2",
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"pytorch": "2.5.1"
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},
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"prompts": {
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"query": "search_query: ",
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