Create README.md (#1)

- Create README.md (6e6b143e9e516f40f8cfb19b8f6b4c2692ad4865)


Co-authored-by: Ghazal Zamaninejad <ghazal-zamani@users.noreply.huggingface.co>

README.md

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+---
+language:
+- fa
+base_model:
+- PartAI/TookaBERT-Large
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+
+This model is a Sentence Transformers model trained for semantic textual similarity and embedding tasks. It maps sentences and paragraphs to a dense vector space, where semantically similar texts are close together.
+
+The model is trained in two sizes: **Base** and **Large**
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install sentence-transformers==3.4.1
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("PartAI/Tooka-SBERT")
+# Run inference
+sentences = [
+    'درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.',
+    'درناها با قامتی بلند و بال‌های پهن، از زیباترین پرندگان مهاجر به شمار می‌روند.',
+    'درناها پرندگانی کوچک با پاهای کوتاه هستند که مهاجرت نمی‌کنند.'
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+## 🛠️ Training Details
+The training is performed in two stages:
+
+1. **Pretraining** on the *Targoman News* dataset  
+2. **Fine-tuning** on multiple synthetic datasets
+
+### Stage 1: Pretraining
+- We use an **asymmetric** setup.
+- Input formatting:
+  - Titles are prepended with `"سوال: "`
+  - Texts are prepended with `"متن: "`
+- Loss function: `CachedMultipleNegativesRankingLoss`
+
+### Stage 2: Fine-tuning
+- Loss functions:
+  - `CachedMultipleNegativesRankingLoss`
+  - `CoSENTLoss`
+- Used across multiple synthetic datasets
+
+
+#  📊 Evaluation
+We evaluate our model on the [**PTEB Benchmark**](https://huggingface.co/spaces/PartAI/pteb-leaderboard). Our model **outperforms mE5-Base on average across PTEB tasks**.
+
+For *Retrieval* and *Reranking* tasks, we follow the same asymmetric structure, prepending:
+- `"سوال: "` to queries  
+- `"متن: "` to documents
+
+
+| Model                                                                          | Pair-Classification-Avg | Classification-Avg | Retrieval-Avg | Reranking-Avg | Overall-Avg |
+|--------------------------------------------------------------------------------|-------------------------|--------------------|---------------|---------------|-------------|
+| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)   | 70.76                   | 69.71              | 63.90         | 76.01         | 69.33       |
+| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 72.55                   | 72.18              | **65.36**     | **78.52**     | **71.44**   |
+| [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3)         | 71.88                   | **79.27**          | 65.18         | 64.62         | 71.37       |
+| tooka-sbert-large-v1                                                           | **81.52**               | 71.54              | 45.61         | 60.44         | 62.54       |
+| tooka-sbert-base-v2                                                            | 75.69                   | 72.16              | 61.24         | 73.40         | 69.49       |
+| tooka-sbert-large-v2                                                           | 80.24                   | 74.73              | 59.80         | 73.44         | 70.54       |
+
+
+### Task-Specific Datasets in PTEB
+
+- **Pair-Classification**:  
+  - FarsTail
+
+- **Classification**:  
+  - MassiveIntentClassification  
+  - MassiveScenarioClassification  
+  - MultilingualSentimentClassification  
+  - PersianFoodSentimentClassification
+
+- **Retrieval**:  
+  - MIRACLRetrieval  
+  - NeuCLIR2023Retrieval  
+  - WikipediaRetrievalMultilingual
+
+- **Reranking**:  
+  - MIRACLReranking  
+  - WikipediaRerankingMultilingual
+
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```