Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:172562
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology") sentences = [ "The current of a stream runs at the rate of 4 kmph. A boat goes 6 km and back to the starting point in 4 hours, then find the speed of the boat in still water?", ")", "a", "[" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Matjac5/MNLP_M3_RAG_MODEL_data_mixture_biology/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |