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 tokenizer.json from codersan/FaMiniLM: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/codersan/FaMiniLM/resolve/main/tokenizer.json
- Command line
-
hf download hf://codersan/FaMiniLM/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/codersan/FaMiniLM/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.