Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:363
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use antonypamo/RRFSAVANTMADE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use antonypamo/RRFSAVANTMADE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("antonypamo/RRFSAVANTMADE") sentences = [ "\\section{5. Correspondencia con constantes físicas}", "\\section{7. Simulación Python – Malla Icosaédrica y Autovalores}", "\\textbf{Interpretación:} Cada modo $n$ representa un posible estado de partícula, resonancia o nodo cognitivo.", "rroc_V" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download model/1_Pooling/config.json from antonypamo/RRFSAVANTMADE: direct link, hf CLI and curl.
- Browser
- Download file 312 Bytes
-
https://huggingface.co/antonypamo/RRFSAVANTMADE/resolve/main/model/1_Pooling/config.json
- Command line
-
hf download hf://antonypamo/RRFSAVANTMADE/model/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/antonypamo/RRFSAVANTMADE/resolve/main/model/1_Pooling/config.json
312 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "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 | |
| } |