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
Update README.md
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README.md
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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## Model Details
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This model, FaMiniLM, was developed alongside FaLaBSE as part of the research paper "MetaRAG and WikiFaQA: A Co-designed Framework and Benchmark for Advancing Persian Long-Context RAG". It serves as a lightweight Persian sentence encoder. FaMiniLM was created by fine-tuning the all-MiniLM-L6-v2 model—which had no prior Persian knowledge—on the custom PersianSimilarSentences dataset. The training was specifically designed to build Persian semantic understanding from the ground up.
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
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