Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Persian
English
bert
feature-extraction
Generated from Trainer
dataset_size:131157
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/FaMiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/FaMiniLM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/FaMiniLM") sentences = [ "عواقب ممنوعیت یادداشت های 500 روپیه و 1000 روپیه در مورد اقتصاد هند چیست؟", "آیا باید در فیزیک و علوم کامپیوتر دو برابر کنم؟", "چگونه اقتصاد هند پس از ممنوعیت 500 1000 یادداشت تحت تأثیر قرار گرفت؟", "آیا آلمان در اجازه پناهندگان سوری به کشور خود اشتباه کرد؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from codersan/FaMiniLM: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/codersan/FaMiniLM/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://codersan/FaMiniLM/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/codersan/FaMiniLM/resolve/main/1_Pooling/config.json
296 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 | |
| } |