Add new SentenceTransformer model

.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

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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

@@ -0,0 +1,605 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:58620066
+- loss:RZTKMatryoshka2dLoss
+base_model: intfloat/multilingual-e5-base
+widget:
+- source_sentence: 'query: чехол на самсунг а30'
+  sentences:
+  - 'passage: Ключниця Shvigel 13989 Коричнева'
+  - 'passage: Ключниці кишенькові Handy Cover Гарантія 1 місяць Колір Рожевий Матеріал
+    Шкіра Країна реєстрації бренда Україна Країна-виробник товару Україна Застібка
+    Змійка Кріплення ключів Кільце'
+  - 'passage: Защитный глянцевый чехол с рисунком для Samsung Galaxy А30 (2019) /
+    A305 / А20 (2019) / A205 Планета'
+- source_sentence: 'query: мебель для кухни'
+  sentences:
+  - 'passage: Стілець для кухні та вітальні Примтекс Плюс 1022 black S-3120 Червоний
+    (ordf)'
+  - 'passage: Повітряні кульки Angel Gifts Гарантія 14 днів Кількість вантажних місць
+    1 Країна реєстрації бренда Китай Кількість предметів, шт 7 Країна-виробник товару
+    Китай Вид Повітряні кулі Розмір 43 см Розмір 40 х 60 см Розмір 30 см Розмір 40
+    см Колір Різнобарвний Матеріал Фольгований поліетилен + латекс Свято День Святого
+    Валентина Свято 8 березня Свято День народження Тип гарантійного талона Гарантія
+    по чеку Можливість доставки Почтомати Форма Фігурна Особливості Фольговані Доставка
+    Premium Доставка Доставка в магазини ROZETKA'
+  - 'passage: Ключница кожаная женская Grande Pelle leather-11353 Красная'
+- source_sentence: 'query: мебель для кухни'
+  sentences:
+  - 'passage: Рідина для систем SCR дизельних двигунів (Евро 4,5,6) 1.5 л SHELL AdBLUE'
+  - 'passage: Ключница Black Brier КЛ-5-17 Черная'
+  - 'passage: Кухонний комплект Злата 2,6м Світ Меблів білий/артвуд світлий (без стільниці)'
+- source_sentence: 'query: вращающаяся подставка для торта'
+  sentences:
+  - 'passage: Штукатурка цементно-вапняна фасадна ПЦШ-017 СІРА ПОЛІПЛАСТ 25 кг'
+  - 'passage: Подставка для работы с тортом вращающаяся 28 см Empire(GS - 840021006274)'
+  - 'passage: Ключницы карманные Dr.Bond Для кого Для женщин Цвет Черный Материал
+    Кожа Застежка Кнопки'
+- source_sentence: 'query: мебель для кухни'
+  sentences:
+  - 'passage: Ключниці кишенькові Karya Гарантія 14 днів Для кого Для жінок Колір
+    Червоний Матеріал Шкіра Країна реєстрації бренда Туреччина Країна-виробник товару
+    Туреччина'
+  - 'passage: Ключница Traum 7203-10 Черная (4820007203107)'
+  - 'passage: Кухня Эко модуль Вытяжка 600 Эверест Ясень Шимо Светлый 60х30х28 см'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the rozetka_positive_pairs dataset. 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Dot Product
+- **Training Dataset:**
+    - rozetka_positive_pairs
+<!-- - **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
+
+```
+RZTKSentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+  (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("rztk/multilingual-e5-base-matryoshka2d-mnr-3")
+# Run inference
+sentences = [
+    'query: мебель для кухни',
+    'passage: Кухня Эко модуль Вытяжка 600 Эверест Ясень Шимо Светлый 60х30х28 см',
+    'passage: Ключниці кишенькові Karya Гарантія 14 днів Для кого Для жінок Колір Червоний Матеріал Шкіра Країна реєстрації бренда Туреччина Країна-виробник товару Туреччина',
+]
+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.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### rozetka_positive_pairs
+
+* Dataset: rozetka_positive_pairs
+* Size: 58,620,066 training samples
+* Columns: <code>query</code> and <code>text</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | text                                                                                |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 11.27 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 59.47 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query                              | text                                                                                                                                                                        |
+  |:-----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>query: xsiomi 9c скло</code> | <code>passage: Защитные стекла  Назначение Для мобильных телефонов Цвет Черный Теги Теги Наличие рамки C рамкой Форм-фактор Плоское Клеевой слой По всей поверхности</code> |
+  | <code>query: xsiomi 9c скло</code> | <code>passage: Захисне скло  Призначення Для мобільних телефонів Колір Чорний Теги Теги Наявність рамки З рамкою Форм-фактор Плоске Клейовий шар По всій поверхні</code>    |
+  | <code>query: xsiomi 9c скло</code> | <code>passage: Захисне скло Glass Full Glue для Xiaomi Redmi 9A/9C/10A (Чорний)</code>                                                                                      |
+* Loss: <code>sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss</code> with these parameters:
+  ```json
+  {
+      "loss": "RZTKMultipleNegativesRankingLoss",
+      "n_layers_per_step": 1,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.0,
+      "kl_div_weight": 1.0,
+      "kl_temperature": 0.3,
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": 1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### rozetka_positive_pairs
+
+* Dataset: rozetka_positive_pairs
+* Size: 1,903,728 evaluation samples
+* Columns: <code>query</code> and <code>text</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | text                                                                               |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 8.36 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 45.68 tokens</li><li>max: 365 tokens</li></ul> |
+* Samples:
+  | query                                       | text                                                                    |
+  |:--------------------------------------------|:------------------------------------------------------------------------|
+  | <code>query: создаем нейронную сеть</code> | <code>passage: Створюємо нейронну мережу</code>                         |
+  | <code>query: создаем нейронную сеть</code> | <code>passage: Создаем нейронную сеть (1666498)</code>                  |
+  | <code>query: создаем нейронную сеть</code> | <code>passage: Научная и техническая литература  Переплет Мягкий</code> |
+* Loss: <code>sentence_transformers_training.model.matryoshka2d_loss.RZTKMatryoshka2dLoss</code> with these parameters:
+  ```json
+  {
+      "loss": "RZTKMultipleNegativesRankingLoss",
+      "n_layers_per_step": 1,
+      "last_layer_weight": 1.0,
+      "prior_layers_weight": 1.0,
+      "kl_div_weight": 1.0,
+      "kl_temperature": 0.3,
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": 1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 88
+- `per_device_eval_batch_size`: 88
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1.0
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `bf16_full_eval`: True
+- `tf32`: True
+- `dataloader_num_workers`: 8
+- `load_best_model_at_end`: True
+- `optim`: adafactor
+- `push_to_hub`: True
+- `hub_model_id`: rztk/multilingual-e5-base-matryoshka2d-mnr-3
+- `hub_private_repo`: True
+- `prompts`: {'query': 'query: ', 'text': 'passage: '}
+- `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`: 88
+- `per_device_eval_batch_size`: 88
+- `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`: 2e-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`: 1.0
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: True
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 8
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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`: adafactor
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: rztk/multilingual-e5-base-matryoshka2d-mnr-3
+- `hub_strategy`: every_save
+- `hub_private_repo`: True
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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`: {'query': 'query: ', 'text': 'passage: '}
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `ddp_static_graph`: False
+- `ddp_comm_hook`: bf16
+- `gradient_as_bucket_view`: False
+- `num_proc`: 30
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss | Validation Loss |
+|:------:|:------:|:-------------:|:---------------:|
+| 0.0050 | 833    | 4.8404        | -               |
+| 0.0100 | 1666   | 4.6439        | -               |
+| 0.0150 | 2499   | 4.2238        | -               |
+| 0.0200 | 3332   | 3.5445        | -               |
+| 0.0250 | 4165   | 2.7514        | -               |
+| 0.0300 | 4998   | 2.4037        | -               |
+| 0.0350 | 5831   | 2.1916        | -               |
+| 0.0400 | 6664   | 2.0938        | -               |
+| 0.0450 | 7497   | 1.9268        | -               |
+| 0.0500 | 8330   | 1.8671        | -               |
+| 0.0550 | 9163   | 1.7069        | -               |
+| 0.0600 | 9996   | 1.6419        | -               |
+| 0.0650 | 10829  | 1.55          | -               |
+| 0.0700 | 11662  | 1.5483        | -               |
+| 0.0750 | 12495  | 1.5419        | -               |
+| 0.0800 | 13328  | 1.3582        | -               |
+| 0.0850 | 14161  | 1.3537        | -               |
+| 0.0900 | 14994  | 1.3067        | -               |
+| 0.0950 | 15827  | 1.2128        | -               |
+| 0.1000 | 16654  | -             | 1.0107          |
+| 0.1000 | 16660  | 1.2248        | -               |
+| 0.1050 | 17493  | 1.1565        | -               |
+| 0.1100 | 18326  | 1.1351        | -               |
+| 0.1150 | 19159  | 1.0808        | -               |
+| 0.1200 | 19992  | 1.0561        | -               |
+| 0.1250 | 20825  | 1.078         | -               |
+| 0.1301 | 21658  | 1.1413        | -               |
+| 0.1351 | 22491  | 1.0446        | -               |
+| 0.1401 | 23324  | 0.9986        | -               |
+| 0.1451 | 24157  | 0.9668        | -               |
+| 0.1501 | 24990  | 0.9753        | -               |
+| 0.1551 | 25823  | 1.0031        | -               |
+| 0.1601 | 26656  | 0.9688        | -               |
+| 0.1651 | 27489  | 0.9262        | -               |
+| 0.1701 | 28322  | 0.9702        | -               |
+| 0.1751 | 29155  | 0.9082        | -               |
+| 0.1801 | 29988  | 0.9264        | -               |
+| 0.1851 | 30821  | 0.8526        | -               |
+| 0.1901 | 31654  | 0.9667        | -               |
+| 0.1951 | 32487  | 0.9421        | -               |
+| 0.2000 | 33308  | -             | 0.6416          |
+| 0.2001 | 33320  | 0.9216        | -               |
+| 0.2051 | 34153  | 0.95          | -               |
+| 0.2101 | 34986  | 0.8895        | -               |
+| 0.2151 | 35819  | 0.8349        | -               |
+| 0.2201 | 36652  | 0.8628        | -               |
+| 0.2251 | 37485  | 0.8729        | -               |
+| 0.2301 | 38318  | 0.9285        | -               |
+| 0.2351 | 39151  | 0.8718        | -               |
+| 0.2401 | 39984  | 0.8792        | -               |
+| 0.2451 | 40817  | 0.8852        | -               |
+| 0.2501 | 41650  | 0.877         | -               |
+| 0.2551 | 42483  | 0.8325        | -               |
+| 0.2601 | 43316  | 0.8446        | -               |
+| 0.2651 | 44149  | 0.812         | -               |
+| 0.2701 | 44982  | 0.8246        | -               |
+| 0.2751 | 45815  | 0.8086        | -               |
+| 0.2801 | 46648  | 0.8553        | -               |
+| 0.2851 | 47481  | 0.8506        | -               |
+| 0.2901 | 48314  | 0.834         | -               |
+| 0.2951 | 49147  | 0.8313        | -               |
+| 0.3000 | 49962  | -             | 0.5377          |
+| 0.3001 | 49980  | 0.8376        | -               |
+| 0.3051 | 50813  | 0.7836        | -               |
+| 0.3101 | 51646  | 0.8089        | -               |
+| 0.3151 | 52479  | 0.8065        | -               |
+| 0.3201 | 53312  | 0.8284        | -               |
+| 0.3251 | 54145  | 0.7959        | -               |
+| 0.3301 | 54978  | 0.8332        | -               |
+| 0.3351 | 55811  | 0.7924        | -               |
+| 0.3401 | 56644  | 0.8171        | -               |
+| 0.3451 | 57477  | 0.7924        | -               |
+| 0.3501 | 58310  | 0.7977        | -               |
+| 0.3551 | 59143  | 0.7729        | -               |
+| 0.3601 | 59976  | 0.7617        | -               |
+| 0.3651 | 60809  | 0.8211        | -               |
+| 0.3701 | 61642  | 0.8497        | -               |
+| 0.3751 | 62475  | 0.8218        | -               |
+| 0.3802 | 63308  | 0.7846        | -               |
+| 0.3852 | 64141  | 0.7876        | -               |
+| 0.3902 | 64974  | 0.7912        | -               |
+| 0.3952 | 65807  | 0.7977        | -               |
+| 0.4000 | 66616  | -             | 0.4974          |
+| 0.4002 | 66640  | 0.8096        | -               |
+| 0.4052 | 67473  | 0.8356        | -               |
+| 0.4102 | 68306  | 0.788         | -               |
+| 0.4152 | 69139  | 0.7683        | -               |
+| 0.4202 | 69972  | 0.7358        | -               |
+| 0.4252 | 70805  | 0.7634        | -               |
+| 0.4302 | 71638  | 0.7535        | -               |
+| 0.4352 | 72471  | 0.756         | -               |
+| 0.4402 | 73304  | 0.7633        | -               |
+| 0.4452 | 74137  | 0.7509        | -               |
+| 0.4502 | 74970  | 0.7547        | -               |
+| 0.4552 | 75803  | 0.7539        | -               |
+| 0.4602 | 76636  | 0.7608        | -               |
+| 0.4652 | 77469  | 0.8262        | -               |
+| 0.4702 | 78302  | 0.8076        | -               |
+| 0.4752 | 79135  | 0.8179        | -               |
+| 0.4802 | 79968  | 0.7709        | -               |
+| 0.4852 | 80801  | 0.744         | -               |
+| 0.4902 | 81634  | 0.7846        | -               |
+| 0.4952 | 82467  | 0.7473        | -               |
+| 0.5000 | 83270  | -             | 0.4776          |
+| 0.5002 | 83300  | 0.7759        | -               |
+| 0.5052 | 84133  | 0.755         | -               |
+| 0.5102 | 84966  | 0.7308        | -               |
+| 0.5152 | 85799  | 0.7256        | -               |
+| 0.5202 | 86632  | 0.7703        | -               |
+| 0.5252 | 87465  | 0.7823        | -               |
+| 0.5302 | 88298  | 0.8109        | -               |
+| 0.5352 | 89131  | 0.7795        | -               |
+| 0.5402 | 89964  | 0.7833        | -               |
+| 0.5452 | 90797  | 0.7752        | -               |
+| 0.5502 | 91630  | 0.7975        | -               |
+| 0.5552 | 92463  | 0.7863        | -               |
+| 0.5602 | 93296  | 0.7337        | -               |
+| 0.5652 | 94129  | 0.7755        | -               |
+| 0.5702 | 94962  | 0.7928        | -               |
+| 0.5752 | 95795  | 0.7604        | -               |
+| 0.5802 | 96628  | 0.7983        | -               |
+| 0.5852 | 97461  | 0.7665        | -               |
+| 0.5902 | 98294  | 0.7749        | -               |
+| 0.5952 | 99127  | 0.7838        | -               |
+| 0.6000 | 99924  | -             | 0.4669          |
+| 0.6002 | 99960  | 0.7727        | -               |
+| 0.6052 | 100793 | 0.8049        | -               |
+| 0.6102 | 101626 | 0.7857        | -               |
+| 0.6152 | 102459 | 0.7622        | -               |
+| 0.6202 | 103292 | 0.8117        | -               |
+| 0.6252 | 104125 | 0.7711        | -               |
+| 0.6302 | 104958 | 0.7892        | -               |
+| 0.6353 | 105791 | 0.7938        | -               |
+| 0.6403 | 106624 | 0.728         | -               |
+| 0.6453 | 107457 | 0.7693        | -               |
+| 0.6503 | 108290 | 0.7875        | -               |
+| 0.6553 | 109123 | 0.7958        | -               |
+| 0.6603 | 109956 | 0.749         | -               |
+| 0.6653 | 110789 | 0.7788        | -               |
+| 0.6703 | 111622 | 0.7614        | -               |
+| 0.6753 | 112455 | 0.7577        | -               |
+| 0.6803 | 113288 | 0.7805        | -               |
+| 0.6853 | 114121 | 0.7677        | -               |
+| 0.6903 | 114954 | 0.7458        | -               |
+| 0.6953 | 115787 | 0.7962        | -               |
+| 0.7000 | 116578 | -             | 0.4641          |
+| 0.7003 | 116620 | 0.7275        | -               |
+| 0.7053 | 117453 | 0.7778        | -               |
+| 0.7103 | 118286 | 0.7885        | -               |
+| 0.7153 | 119119 | 0.8046        | -               |
+| 0.7203 | 119952 | 0.8222        | -               |
+| 0.7253 | 120785 | 0.7714        | -               |
+| 0.7303 | 121618 | 0.7983        | -               |
+| 0.7353 | 122451 | 0.7359        | -               |
+| 0.7403 | 123284 | 0.7618        | -               |
+| 0.7453 | 124117 | 0.783         | -               |
+| 0.7503 | 124950 | 0.763         | -               |
+| 0.7553 | 125783 | 0.809         | -               |
+| 0.7603 | 126616 | 0.794         | -               |
+| 0.7653 | 127449 | 0.7366        | -               |
+| 0.7703 | 128282 | 0.776         | -               |
+| 0.7753 | 129115 | 0.8053        | -               |
+| 0.7803 | 129948 | 0.7941        | -               |
+| 0.7853 | 130781 | 0.7722        | -               |
+| 0.7903 | 131614 | 0.7959        | -               |
+| 0.7953 | 132447 | 0.8061        | -               |
+| 0.8000 | 133232 | -             | 0.4468          |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.3.0
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.1.1
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,28 @@
+{
+  "_name_or_path": "rztk/multilingual-e5-base-matryoshka2d-mnr-3",
+  "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": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,13 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.0",
+    "transformers": "4.45.1",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {
+    "query": "query: ",
+    "text": "passage: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e759dd53bcd8b3994093d7792e7eae011866cd1bafcc1c2fc331dfc35b20de8
+size 1112197096

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "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 sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+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,
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+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "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": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}