Instructions to use AutoDataBench/Retrieval-resources with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AutoDataBench/Retrieval-resources with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AutoDataBench/Retrieval-resources") 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] - Notebooks
- Google Colab
- Kaggle
Download models/Qwen3-Embedding-0.6B/1_Pooling/config.json from AutoDataBench/Retrieval-resources: direct link, hf CLI and curl.
- Browser
- Download file 313 Bytes
-
https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/Qwen3-Embedding-0.6B/1_Pooling/config.json
- Command line
-
hf download hf://AutoDataBench/Retrieval-resources/models/Qwen3-Embedding-0.6B/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/AutoDataBench/Retrieval-resources/resolve/main/models/Qwen3-Embedding-0.6B/1_Pooling/config.json
313 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |