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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OrcaDB/cde-small-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from OrcaDB/cde-small-v1: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/OrcaDB/cde-small-v1/resolve/main/config.json
- Command line
-
hf download hf://OrcaDB/cde-small-v1/config.json
-
curl -L -o config.json https://huggingface.co/OrcaDB/cde-small-v1/resolve/main/config.json
1.1 kB
| { | |
| "_name_or_path": "jxm/cde-small-v1", | |
| "architecture": "transductive", | |
| "architectures": [ | |
| "DatasetTransformer" | |
| ], | |
| "attn_implementation": null, | |
| "auto_map": { | |
| "AutoConfig": "jxm/cde-small-v1--misc.ContextualModelConfig", | |
| "AutoModel": "jxm/cde-small-v1--model.DatasetTransformer" | |
| }, | |
| "biencoder_pooling_strategy": "mean", | |
| "cache_dir": null, | |
| "config_name": null, | |
| "disable_dropout": true, | |
| "disable_transductive_rotary_embedding": true, | |
| "document_prompt": "search_document: ", | |
| "embedder": "nomic-ai/nomic-bert-2048", | |
| "embedder_rerank": "sentence-transformers/gtr-t5-base", | |
| "embedding_output_dim": null, | |
| "embedding_size": 768, | |
| "limit_layers": null, | |
| "limit_layers_first_stage": null, | |
| "logit_scale": 50.0, | |
| "max_seq_length": 512, | |
| "model_revision": "main", | |
| "query_prompt": "search_query: ", | |
| "tokenizer_name": null, | |
| "torch_dtype": "float32", | |
| "transductive_corpus_size": 512, | |
| "transductive_sequence_dropout_prob": 0.0, | |
| "transductive_tie_token_embeddings": false, | |
| "transductive_tokens_per_document": 1, | |
| "transformers_version": "4.45.2" | |
| } | |