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@@ -35,12 +35,12 @@ base_model:
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  - PartAI/TookaBERT-Base
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  ---
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- # TookaSBERT-Base1
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  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.
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- The model is trained in two sizes: **Base** and **Large**
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  ## Usage
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@@ -57,7 +57,7 @@ Then you can load this model and run inference.
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  from sentence_transformers import SentenceTransformer
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  # Download from the 🤗 Hub
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- model = SentenceTransformer("PartAI/TookaSBERT-Base1")
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  # Run inference
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  sentences = [
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  'درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.',
@@ -102,14 +102,13 @@ For *Retrieval* and *Reranking* tasks, we follow the same asymmetric structure,
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  - `"متن: "` to documents
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- | Model | #Params | Pair-Classification-Avg | Classification-Avg | Retrieval-Avg | Reranking-Avg | Tasks-Avg |
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- |--------------------------------------------------------------------------------|:-------:|-------------------------|--------------------|---------------|---------------|-----------|
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- | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 278M | 70.76 | 69.71 | 63.90 | 76.01 | 70.09 |
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- | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 72.55 | 72.18 | **65.36** | **78.52** | **72.15** |
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- | [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) | 572M | 71.88 | **79.27** | 65.18 | 64.62 | 70.24 |
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- | tooka-sbert-large-v1 | 353M | **81.52** | 71.54 | 45.61 | 60.44 | 64.78 |
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- | tooka-sbert-base-v2 | 123M | 75.69 | 72.16 | 61.24 | 73.40 | 70.62 |
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- | tooka-sbert-large-v2 | 353M | 80.24 | 74.73 | 59.80 | 73.44 | 72.05 |
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  ### Task-Specific Datasets in PTEB
 
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  - PartAI/TookaBERT-Base
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  ---
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+ # Tooka-SBERT-V2-Small
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  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.
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+ The model is trained in two sizes: **Small** and **Large**
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  ## Usage
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  from sentence_transformers import SentenceTransformer
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  # Download from the 🤗 Hub
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+ model = SentenceTransformer("PartAI/Tooka-SBERT-V2-Small")
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  # Run inference
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  sentences = [
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  'درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.',
 
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  - `"متن: "` to documents
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+ | Model | #Params | Pair-Classification-Avg | Classification-Avg | Retrieval-Avg | Reranking-Avg | CrossTasks-Avg |
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+ |--------------------------------------------------------------------------------|:-------:|-------------------------|--------------------|---------------|---------------|----------------|
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+ | [Tooka-SBERT-V2-Large](https://huggingface.co/PartAI/Tooka-SBERT-V2-Large) | 353M | 80.24 | 74.73 | 59.80 | 73.44 | **72.05** |
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+ | [Tooka-SBERT-V2-Small](https://huggingface.co/PartAI/Tooka-SBERT-V2-Small) | 123M | 75.69 | 72.16 | 61.24 | 73.40 | 70.62 |
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+ | [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) | 572M | 71.88 | **79.27** | **65.18** | 64.62 | 70.24 |
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+ | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 278M | 70.76 | 69.71 | 63.90 | **76.01** | 70.09 |
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+ | [Tooka-SBERT-V1-Large](https://huggingface.co/PartAI/Tooka-SBERT) | 353M | **81.52** | 71.54 | 45.61 | 60.44 | 64.78 |
 
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  ### Task-Specific Datasets in PTEB