Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:801402
+- loss:GISTEmbedLoss
+base_model: heydariAI/persian-embeddings
+widget:
+- source_sentence: پروژکتور خیابانی 100 وات
+  sentences:
+  - پروژکتور پارکی 100 وات ضد آب
+  - آیفون مینی
+  - لامپ خیابانی 100 وات LED
+- source_sentence: اسپری ابرسان بیواکوا
+  sentences:
+  - اسپری آبرسان صورت بیواکوا
+  - چراغ نفتی آشپزخانه سولان
+  - کرم مرطوب کننده بیواکوا
+- source_sentence: شلوار بگ آبی
+  sentences:
+  - شلوار راحتی مردانه بگو
+  - پیراهن بگ آبی مردانه
+  - شلوار جین بگ آبی مردانه
+- source_sentence: ورزشی mma
+  sentences:
+  - کاسه داخلی زودپز
+  - لباس ورزشی دوچرخه سواری
+  - دستکش بوکس و ام‌ام‌ا
+- source_sentence: پنکه رومیزی
+  sentences:
+  - چراغ رومیزی
+  - میله یو یوگا
+  - پنکه رومیزی کوچک
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: xml-base base trained on Query triplets
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: query dev
+      type: query-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9675767421722412
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: query test
+      type: query-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9668284058570862
+      name: Cosine Accuracy
+---
+
+# xml-base base trained on Query triplets
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [heydariAI/persian-embeddings](https://huggingface.co/heydariAI/persian-embeddings) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [heydariAI/persian-embeddings](https://huggingface.co/heydariAI/persian-embeddings) <!-- at revision 0c487ec2e3838e4b348ffac7281dc1f6e5fa2453 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 128, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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 = [
+    'پنکه رومیزی',
+    'پنکه رومیزی کوچک',
+    'چراغ رومیزی',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8511, 0.2971],
+#         [0.8511, 1.0000, 0.2242],
+#         [0.2971, 0.2242, 1.0000]])
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Datasets: `query-dev` and `query-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | query-dev  | query-test |
+|:--------------------|:-----------|:-----------|
+| **cosine_accuracy** | **0.9676** | **0.9668** |
+
+<!--
+## 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.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 801,402 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         | negative                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.86 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.13 tokens</li><li>max: 16 tokens</li></ul> |
+* Samples:
+  | anchor                              | positive                                      | negative                              |
+  |:------------------------------------|:----------------------------------------------|:--------------------------------------|
+  | <code>حراجی لباس بچه</code>         | <code>لباس بچگانه حراجی</code>                | <code>حراجی کفش زنانه</code>          |
+  | <code>گوشواره طلا دو حلقه اس</code> | <code>گوشواره طلا زنانه دو حلقه</code>        | <code>انگشتر طلا زنانه دو بندی</code> |
+  | <code>redmy a3قاب گوشی</code>       | <code>قاب گوشی مناسب برای گوشی ردمی A3</code> | <code>شارژر گوشی ردمی A3</code>       |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {
+      "guide": "SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')",
+      "temperature": 0.0493,
+      "margin_strategy": "relative",
+      "margin": 0.0516,
+      "contrast_anchors": true,
+      "contrast_positives": true,
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### json
+
+* Dataset: json
+* Size: 100,175 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                          | positive                                                                         | negative                                                                         |
+  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 7.8 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.86 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.09 tokens</li><li>max: 16 tokens</li></ul> |
+* Samples:
+  | anchor                                     | positive                                           | negative                                  |
+  |:-------------------------------------------|:---------------------------------------------------|:------------------------------------------|
+  | <code>کراپ تیشرت زنانه ورزشی</code>        | <code>تیشرت کراپ زنانه ورزشی</code>                | <code>شلوار ورزشی زنانه</code>            |
+  | <code>فیشیال دستگاه</code>                 | <code>دستگاه بخور صورت برای فیشیال</code>          | <code>دستگاه تصفیه هوای خانگی</code>      |
+  | <code>پیراهن مشکی مردانه یقه خرگوشی</code> | <code>پیراهن مردانه مشکی یقه دار طرح خرگوشی</code> | <code>شلوار مشکی مردانه یقه خرگوشی</code> |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {
+      "guide": "SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')",
+      "temperature": 0.0493,
+      "margin_strategy": "relative",
+      "margin": 0.0516,
+      "contrast_anchors": true,
+      "contrast_positives": true,
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 1.1701480000238433e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.15873389962653162
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 1.1701480000238433e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.15873389962653162
+- `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`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 3
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `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}
+- `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
+- `hub_revision`: None
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss | query-dev_cosine_accuracy | query-test_cosine_accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:-------------------------:|:--------------------------:|
+| -1     | -1    | -             | -               | 0.8824                    | -                          |
+| 0.0799 | 1000  | 0.2209        | 0.1147          | 0.9180                    | -                          |
+| 0.1597 | 2000  | 0.1248        | 0.0842          | 0.9316                    | -                          |
+| 0.2396 | 3000  | 0.0962        | 0.0693          | 0.9370                    | -                          |
+| 0.3195 | 4000  | 0.0842        | 0.0611          | 0.9426                    | -                          |
+| 0.3993 | 5000  | 0.0742        | 0.0555          | 0.9458                    | -                          |
+| 0.4792 | 6000  | 0.0681        | 0.0538          | 0.9490                    | -                          |
+| 0.5591 | 7000  | 0.0661        | 0.0498          | 0.9488                    | -                          |
+| 0.6389 | 8000  | 0.0637        | 0.0471          | 0.9525                    | -                          |
+| 0.7188 | 9000  | 0.0609        | 0.0461          | 0.9528                    | -                          |
+| 0.7987 | 10000 | 0.0573        | 0.0452          | 0.9525                    | -                          |
+| 0.8785 | 11000 | 0.055         | 0.0449          | 0.9550                    | -                          |
+| 0.9584 | 12000 | 0.0541        | 0.0431          | 0.9556                    | -                          |
+| 1.0383 | 13000 | 0.0553        | 0.0427          | 0.9547                    | -                          |
+| 1.1181 | 14000 | 0.053         | 0.0402          | 0.9586                    | -                          |
+| 1.1980 | 15000 | 0.0464        | 0.0401          | 0.9583                    | -                          |
+| 1.2779 | 16000 | 0.0437        | 0.0380          | 0.9586                    | -                          |
+| 1.3577 | 17000 | 0.0426        | 0.0373          | 0.9599                    | -                          |
+| 1.4376 | 18000 | 0.038         | 0.0376          | 0.9593                    | -                          |
+| 1.5175 | 19000 | 0.037         | 0.0361          | 0.9605                    | -                          |
+| 1.5973 | 20000 | 0.0348        | 0.0364          | 0.9607                    | -                          |
+| 1.6772 | 21000 | 0.033         | 0.0349          | 0.9621                    | -                          |
+| 1.7570 | 22000 | 0.029         | 0.0347          | 0.9609                    | -                          |
+| 1.8369 | 23000 | 0.0278        | 0.0345          | 0.9617                    | -                          |
+| 1.9168 | 24000 | 0.0261        | 0.0346          | 0.9620                    | -                          |
+| 1.9966 | 25000 | 0.0269        | 0.0334          | 0.9626                    | -                          |
+| 2.0765 | 26000 | 0.0267        | 0.0335          | 0.9632                    | -                          |
+| 2.1564 | 27000 | 0.0246        | 0.0333          | 0.9643                    | -                          |
+| 2.2362 | 28000 | 0.0227        | 0.0330          | 0.9629                    | -                          |
+| 2.3161 | 29000 | 0.0224        | 0.0327          | 0.9642                    | -                          |
+| 2.3960 | 30000 | 0.0209        | 0.0325          | 0.9642                    | -                          |
+| 2.4758 | 31000 | 0.0195        | 0.0330          | 0.9648                    | -                          |
+| 2.5557 | 32000 | 0.0191        | 0.0327          | 0.9652                    | -                          |
+| 2.6356 | 33000 | 0.0189        | 0.0316          | 0.9643                    | -                          |
+| 2.7154 | 34000 | 0.0165        | 0.0324          | 0.9645                    | -                          |
+| 2.7953 | 35000 | 0.015         | 0.0309          | 0.9644                    | -                          |
+| 2.8752 | 36000 | 0.0142        | 0.0323          | 0.9654                    | -                          |
+| 2.9550 | 37000 | 0.0139        | 0.0316          | 0.9646                    | -                          |
+| 3.0349 | 38000 | 0.0151        | 0.0303          | 0.9650                    | -                          |
+| 3.1148 | 39000 | 0.0145        | 0.0307          | 0.9664                    | -                          |
+| 3.1946 | 40000 | 0.0128        | 0.0303          | 0.9656                    | -                          |
+| 3.2745 | 41000 | 0.0127        | 0.0300          | 0.9659                    | -                          |
+| 3.3544 | 42000 | 0.0125        | 0.0305          | 0.9663                    | -                          |
+| 3.4342 | 43000 | 0.0106        | 0.0305          | 0.9661                    | -                          |
+| 3.5141 | 44000 | 0.011         | 0.0308          | 0.9670                    | -                          |
+| 3.5940 | 45000 | 0.0105        | 0.0295          | 0.9665                    | -                          |
+| 3.6738 | 46000 | 0.0101        | 0.0297          | 0.9666                    | -                          |
+| 3.7537 | 47000 | 0.0091        | 0.0299          | 0.9667                    | -                          |
+| 3.8336 | 48000 | 0.009         | 0.0297          | 0.9666                    | -                          |
+| 3.9134 | 49000 | 0.0082        | 0.0298          | 0.9662                    | -                          |
+| 3.9933 | 50000 | 0.0086        | 0.0301          | 0.9668                    | -                          |
+| 4.0732 | 51000 | 0.0087        | 0.0290          | 0.9674                    | -                          |
+| 4.1530 | 52000 | 0.0084        | 0.0287          | 0.9678                    | -                          |
+| 4.2329 | 53000 | 0.0078        | 0.0288          | 0.9667                    | -                          |
+| 4.3128 | 54000 | 0.008         | 0.0287          | 0.9669                    | -                          |
+| 4.3926 | 55000 | 0.0074        | 0.0287          | 0.9669                    | -                          |
+| 4.4725 | 56000 | 0.007         | 0.0288          | 0.9677                    | -                          |
+| 4.5524 | 57000 | 0.0068        | 0.0288          | 0.9674                    | -                          |
+| 4.6322 | 58000 | 0.007         | 0.0282          | 0.9677                    | -                          |
+| 4.7121 | 59000 | 0.0064        | 0.0286          | 0.9678                    | -                          |
+| 4.7919 | 60000 | 0.006         | 0.0283          | 0.9675                    | -                          |
+| 4.8718 | 61000 | 0.0059        | 0.0284          | 0.9675                    | -                          |
+| 4.9517 | 62000 | 0.0057        | 0.0284          | 0.9676                    | -                          |
+| -1     | -1    | -             | -               | 0.9676                    | 0.9668                     |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.0
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.10.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.0",
+    "pytorch": "2.7.1+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1fffb78289e413ff179e56d60180780dd12cb19e066b276b61cc78a498333dd
+size 2239607176

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 128,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}