Update README.md

README.md

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----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:80000
-- loss:MultipleNegativesRankingLoss
-base_model: Alibaba-NLP/gte-multilingual-base
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
-datasets:
-- mshojaei77/Persian_sft
-language:
-- fa
----
-
-# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Output Dimensionality:** 768 dimensions
-- **Similarity Function:** Cosine Similarity
-- **Training Dataset:** [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft)
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'برج میلاد در تهران هست',
-   'یکی از برج های مسکونی تهران برج تهران است',
-   'تهران برج های زیادی دارد'
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-
-#### Persian_sft Dataset
-This model was also fine-tuned on the [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft) dataset.
-
-
-* Size: 80,000 training samples
-* Columns: <code>sentence_0</code> and <code>sentence_1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                         | sentence_1                                                                         |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 19.27 tokens</li><li>max: 276 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 90.8 tokens</li><li>max: 514 tokens</li></ul> |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `fp16`: bf16
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1
-- `num_train_epochs`: 3
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: False
-- `fp16`: bf16
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `tp_size`: 0
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: None
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `include_for_metrics`: []
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `use_liger_kernel`: False
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Framework Versions
-- Python: 3.10.8
-- Sentence Transformers: 4.1.0
-- Transformers: 4.51.3
-- PyTorch: 2.7.0+cu126
-- Accelerate: 1.6.0
-- Datasets: 3.6.0
-- Tokenizers: 0.21.1
-
-Special thanks to [mshojaei77](https://huggingface.co/mshojaei77) for providing the `Persian_sft` dataset used in fine-tuning this model.
-
-## 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",
-}
-```
-
-#### MultipleNegativesRankingLoss
-```bibtex
-@misc{henderson2017efficient,
-    title={Efficient Natural Language Response Suggestion for Smart Reply},
-    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
-    year={2017},
-    eprint={1705.00652},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-## Model Card Contact
-
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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:80000
+- loss:MultipleNegativesRankingLoss
+base_model: Alibaba-NLP/gte-multilingual-base
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+datasets:
+- mshojaei77/Persian_sft
+language:
+- fa
+---
+
+# maux-gte-persian-v3 (fp16)
+**A high-performance Persian sentence embedding model based on Alibaba-NLP/gte-multilingual-base, released in fp16 for efficient inference.**
+
+---
+
+## Model Overview
+
+This is the **fp16 (half-precision)** version of [maux-gte-persian-v3](https://huggingface.co/xmanii/maux-gte-persian-v3), a Sentence Transformers model fine-tuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) for robust Persian sentence and paragraph embeddings.  
+The fp16 format enables faster and more memory-efficient inference, especially on modern GPUs.
+
+**Key Features:**
+- **Base Model:** Alibaba-NLP/gte-multilingual-base
+- **Fine-tuned on:** [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft) (80,000 Persian sentence pairs)
+- **Output Dimension:** 768
+- **Max Sequence Length:** 8192 tokens
+- **Similarity Function:** Cosine Similarity
+- **Loss Function:** MultipleNegativesRankingLoss
+- **Format:** fp16 (`model.safetensors`)
+
+---
+
+## Performance
+
+- **Excellent performance** on Persian semantic similarity, search, and clustering tasks.
+- **Outperforms** or matches [jinaai-v3](https://huggingface.co/jinaai/jina-embeddings-v3-base-fa) in most Persian benchmarks (see [comparison charts](./jinaai_v3_vs_maux_v3_comparison.png)).
+- Efficient for large-scale inference due to fp16 weights.
+
+---
+
+## Model Architecture
+
+```python
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, ...})
+  (2): Normalize()
+)
+```
+
+---
+
+## Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("xmanii/maux-gte-persian-v3-fp16", trust_remote_code=True)
+sentences = [
+    'برج میلاد در تهران هست',
+    'یکی از برج های مسکونی تهران برج تهران است',
+    'تهران برج های زیادی دارد'
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)  # [3, 768]
+
+# Compute cosine similarity
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)  # [3, 3]
+```
+
+---
+
+## Training Details
+
+- **Dataset:** [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft)
+- **Loss:** MultipleNegativesRankingLoss (scale=20.0, similarity_fct="cos_sim")
+- **Batch size:** 64
+- **Precision:** bf16 during training, fp16 for this release
+- **Frameworks:** Python 3.10, Sentence Transformers 4.1.0, Transformers 4.51.3, PyTorch 2.7.0+cu126
+
+---
+
+## Files
+
+- `model.safetensors` (fp16 weights)
+- All necessary config and tokenizer files
+- Custom code: `modeling.py`, `configuration.py` (required for loading)
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+```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",
+}
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+---
+
+## Acknowledgements
+
+- Special thanks to [mshojaei77](https://huggingface.co/mshojaei77) for the Persian_sft dataset.
+- Built on top of [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base).
+
+---
+
+## License
+
+This model is distributed under the same license as the base model and dataset.
+
+---
+
+**For questions or feedback, please open an issue or discussion on the Hugging Face model page.**