Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:234000
+- loss:MSELoss
+base_model: google-bert/bert-base-multilingual-uncased
+widget:
+- source_sentence: who sings in spite of ourselves with john prine
+  sentences:
+  - es
+  - når ble michael jordan draftet til nba
+  - quien canta en spite of ourselves con john prine
+- source_sentence: who wrote when you look me in the eyes
+  sentences:
+  - متى بدأت الفتاة الكشفية في بيع ملفات تعريف الارتباط
+  - A écrit when you look me in the eyes
+  - fr
+- source_sentence: when was fathers day made a national holiday
+  sentences:
+  - wann wurde der Vatertag zum nationalen Feiertag
+  - de
+  - ' អ្នកណាច្រៀង i want to sing you a love song'
+- source_sentence: what is the density of the continental crust
+  sentences:
+  - cuál es la densidad de la corteza continental
+  - wie zingt i want to sing you a love song
+  - es
+- source_sentence: who wrote the song i shot the sheriff
+  sentences:
+  - Quel est l'âge légal pour consommer du vin au Canada?
+  - i shot the sheriff şarkısını kim besteledi
+  - tr
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- negative_mse
+model-index:
+- name: SentenceTransformer based on google-bert/bert-base-multilingual-uncased
+  results:
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to ar
+      type: MSE-val-en-to-ar
+    metrics:
+    - type: negative_mse
+      value: -20.37721574306488
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to da
+      type: MSE-val-en-to-da
+    metrics:
+    - type: negative_mse
+      value: -17.167489230632782
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to de
+      type: MSE-val-en-to-de
+    metrics:
+    - type: negative_mse
+      value: -17.10948944091797
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to en
+      type: MSE-val-en-to-en
+    metrics:
+    - type: negative_mse
+      value: -15.333698689937592
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to es
+      type: MSE-val-en-to-es
+    metrics:
+    - type: negative_mse
+      value: -16.898061335086823
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to fi
+      type: MSE-val-en-to-fi
+    metrics:
+    - type: negative_mse
+      value: -18.428558111190796
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to fr
+      type: MSE-val-en-to-fr
+    metrics:
+    - type: negative_mse
+      value: -17.04207956790924
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to he
+      type: MSE-val-en-to-he
+    metrics:
+    - type: negative_mse
+      value: -19.942057132720947
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to hu
+      type: MSE-val-en-to-hu
+    metrics:
+    - type: negative_mse
+      value: -18.757066130638123
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to it
+      type: MSE-val-en-to-it
+    metrics:
+    - type: negative_mse
+      value: -17.18708872795105
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to ja
+      type: MSE-val-en-to-ja
+    metrics:
+    - type: negative_mse
+      value: -19.915536046028137
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to ko
+      type: MSE-val-en-to-ko
+    metrics:
+    - type: negative_mse
+      value: -21.39919400215149
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to km
+      type: MSE-val-en-to-km
+    metrics:
+    - type: negative_mse
+      value: -28.658682107925415
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to ms
+      type: MSE-val-en-to-ms
+    metrics:
+    - type: negative_mse
+      value: -17.25209951400757
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to nl
+      type: MSE-val-en-to-nl
+    metrics:
+    - type: negative_mse
+      value: -16.605134308338165
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to no
+      type: MSE-val-en-to-no
+    metrics:
+    - type: negative_mse
+      value: -17.149969935417175
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to pl
+      type: MSE-val-en-to-pl
+    metrics:
+    - type: negative_mse
+      value: -17.846450209617615
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to pt
+      type: MSE-val-en-to-pt
+    metrics:
+    - type: negative_mse
+      value: -17.19353199005127
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to ru
+      type: MSE-val-en-to-ru
+    metrics:
+    - type: negative_mse
+      value: -18.13419610261917
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to sv
+      type: MSE-val-en-to-sv
+    metrics:
+    - type: negative_mse
+      value: -17.13200956583023
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to th
+      type: MSE-val-en-to-th
+    metrics:
+    - type: negative_mse
+      value: -26.43084228038788
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to tr
+      type: MSE-val-en-to-tr
+    metrics:
+    - type: negative_mse
+      value: -18.183308839797974
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to vi
+      type: MSE-val-en-to-vi
+    metrics:
+    - type: negative_mse
+      value: -18.749597668647766
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to zh cn
+      type: MSE-val-en-to-zh_cn
+    metrics:
+    - type: negative_mse
+      value: -18.811793625354767
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to zh hk
+      type: MSE-val-en-to-zh_hk
+    metrics:
+    - type: negative_mse
+      value: -18.54081153869629
+      name: Negative Mse
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: MSE val en to zh tw
+      type: MSE-val-en-to-zh_tw
+    metrics:
+    - type: negative_mse
+      value: -19.14038509130478
+      name: Negative Mse
+---
+
+# SentenceTransformer based on google-bert/bert-base-multilingual-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased). 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:** [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) <!-- at revision 7cbf9a625e29989f6b9c6c2fa68234c304f7e38f -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (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})
+)
+```
+
+## 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("luanafelbarros/bert-base-multilingual-uncased-matryoshka-mkqa")
+# Run inference
+sentences = [
+    'who wrote the song i shot the sheriff',
+    'i shot the sheriff şarkısını kim besteledi',
+    'tr',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Knowledge Distillation
+
+* Datasets: `MSE-val-en-to-ar`, `MSE-val-en-to-da`, `MSE-val-en-to-de`, `MSE-val-en-to-en`, `MSE-val-en-to-es`, `MSE-val-en-to-fi`, `MSE-val-en-to-fr`, `MSE-val-en-to-he`, `MSE-val-en-to-hu`, `MSE-val-en-to-it`, `MSE-val-en-to-ja`, `MSE-val-en-to-ko`, `MSE-val-en-to-km`, `MSE-val-en-to-ms`, `MSE-val-en-to-nl`, `MSE-val-en-to-no`, `MSE-val-en-to-pl`, `MSE-val-en-to-pt`, `MSE-val-en-to-ru`, `MSE-val-en-to-sv`, `MSE-val-en-to-th`, `MSE-val-en-to-tr`, `MSE-val-en-to-vi`, `MSE-val-en-to-zh_cn`, `MSE-val-en-to-zh_hk` and `MSE-val-en-to-zh_tw`
+* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
+
+| Metric           | MSE-val-en-to-ar | MSE-val-en-to-da | MSE-val-en-to-de | MSE-val-en-to-en | MSE-val-en-to-es | MSE-val-en-to-fi | MSE-val-en-to-fr | MSE-val-en-to-he | MSE-val-en-to-hu | MSE-val-en-to-it | MSE-val-en-to-ja | MSE-val-en-to-ko | MSE-val-en-to-km | MSE-val-en-to-ms | MSE-val-en-to-nl | MSE-val-en-to-no | MSE-val-en-to-pl | MSE-val-en-to-pt | MSE-val-en-to-ru | MSE-val-en-to-sv | MSE-val-en-to-th | MSE-val-en-to-tr | MSE-val-en-to-vi | MSE-val-en-to-zh_cn | MSE-val-en-to-zh_hk | MSE-val-en-to-zh_tw |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:--------------------|:--------------------|:--------------------|
+| **negative_mse** | **-20.3772**     | **-17.1675**     | **-17.1095**     | **-15.3337**     | **-16.8981**     | **-18.4286**     | **-17.0421**     | **-19.9421**     | **-18.7571**     | **-17.1871**     | **-19.9155**     | **-21.3992**     | **-28.6587**     | **-17.2521**     | **-16.6051**     | **-17.15**       | **-17.8465**     | **-17.1935**     | **-18.1342**     | **-17.132**      | **-26.4308**     | **-18.1833**     | **-18.7496**     | **-18.8118**        | **-18.5408**        | **-19.1404**        |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+
+* Size: 234,000 training samples
+* Columns: <code>english</code>, <code>non-english</code>, <code>target</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non-english                                                                       | target                                                                          | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                          | list                                 |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.48 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.27 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                          | non-english                                             | target          | label                                                                                                                   |
+  |:-------------------------------------------------|:--------------------------------------------------------|:----------------|:------------------------------------------------------------------------------------------------------------------------|
+  | <code>who plays hope on days of our lives</code> | <code>من الذي يلعب الأمل في أيام حياتنا</code>          | <code>ar</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
+  | <code>who plays hope on days of our lives</code> | <code>hvem spiller hope i Horton-sagaen</code>          | <code>da</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
+  | <code>who plays hope on days of our lives</code> | <code>Wer spielt die Hope in Zeit der Sehnsucht?</code> | <code>de</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 13,000 evaluation samples
+* Columns: <code>english</code>, <code>non-english</code>, <code>target</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non-english                                                                       | target                                                                          | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                          | list                                 |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.53 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.37 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                     | non-english                                                     | target          | label                                                                                                                        |
+  |:------------------------------------------------------------|:----------------------------------------------------------------|:----------------|:-----------------------------------------------------------------------------------------------------------------------------|
+  | <code>who played prudence on nanny and the professor</code> | <code>من لعب دور "prudence" فى "nanny and the professor"</code> | <code>ar</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
+  | <code>who played prudence on nanny and the professor</code> | <code>hvem spiller prudence på nanny and the professor</code>   | <code>da</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
+  | <code>who played prudence on nanny and the professor</code> | <code>Wer spielte Prudence in Nanny and the Professor</code>    | <code>de</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 1e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### 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`: 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`: 1e-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
+- `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`: False
+- `fp16`: True
+- `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}
+- `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`: False
+- `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`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | MSE-val-en-to-ar_negative_mse | MSE-val-en-to-da_negative_mse | MSE-val-en-to-de_negative_mse | MSE-val-en-to-en_negative_mse | MSE-val-en-to-es_negative_mse | MSE-val-en-to-fi_negative_mse | MSE-val-en-to-fr_negative_mse | MSE-val-en-to-he_negative_mse | MSE-val-en-to-hu_negative_mse | MSE-val-en-to-it_negative_mse | MSE-val-en-to-ja_negative_mse | MSE-val-en-to-ko_negative_mse | MSE-val-en-to-km_negative_mse | MSE-val-en-to-ms_negative_mse | MSE-val-en-to-nl_negative_mse | MSE-val-en-to-no_negative_mse | MSE-val-en-to-pl_negative_mse | MSE-val-en-to-pt_negative_mse | MSE-val-en-to-ru_negative_mse | MSE-val-en-to-sv_negative_mse | MSE-val-en-to-th_negative_mse | MSE-val-en-to-tr_negative_mse | MSE-val-en-to-vi_negative_mse | MSE-val-en-to-zh_cn_negative_mse | MSE-val-en-to-zh_hk_negative_mse | MSE-val-en-to-zh_tw_negative_mse |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:--------------------------------:|:--------------------------------:|:--------------------------------:|
+| 0.1367 | 500  | 0.3588        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.2734 | 1000 | 0.3078        | 0.2868          | -27.3597                      | -26.5326                      | -26.5313                      | -26.0601                      | -26.4280                      | -26.8319                      | -26.4885                      | -27.1627                      | -26.9695                      | -26.5628                      | -27.2583                      | -27.7239                      | -31.2177                      | -26.6501                      | -26.4197                      | -26.4809                      | -26.6655                      | -26.4345                      | -26.6570                      | -26.5526                      | -30.4823                      | -26.9554                      | -27.1040                      | -27.0230                         | -26.9012                         | -27.0515                         |
+| 0.4102 | 1500 | 0.2846        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.5469 | 2000 | 0.2707        | 0.2617          | -24.6096                      | -22.8821                      | -22.8752                      | -21.8660                      | -22.7026                      | -23.6128                      | -22.7468                      | -24.2281                      | -23.6469                      | -22.9147                      | -24.3616                      | -25.2999                      | -30.4061                      | -23.0865                      | -22.5916                      | -22.8392                      | -23.1451                      | -22.7741                      | -23.2652                      | -22.9440                      | -29.2747                      | -23.5285                      | -23.8786                      | -23.6384                         | -23.5170                         | -23.8081                         |
+| 0.6836 | 2500 | 0.2613        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.8203 | 3000 | 0.2542        | 0.2491          | -23.2261                      | -21.0314                      | -20.9970                      | -19.7599                      | -20.8388                      | -21.9791                      | -20.8374                      | -22.8299                      | -22.0605                      | -21.0367                      | -22.9281                      | -24.1290                      | -29.9238                      | -21.2195                      | -20.6506                      | -20.9939                      | -21.4204                      | -20.9651                      | -21.5594                      | -21.0815                      | -28.3947                      | -21.8046                      | -22.2153                      | -21.9866                         | -21.8474                         | -22.1930                         |
+| 0.9571 | 3500 | 0.248         | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.0938 | 4000 | 0.2438        | 0.2420          | -22.4435                      | -19.9880                      | -19.9588                      | -18.5856                      | -19.7880                      | -20.9892                      | -19.8194                      | -21.9951                      | -21.1703                      | -19.9940                      | -22.1052                      | -23.3569                      | -29.5927                      | -20.1685                      | -19.5862                      | -19.9676                      | -20.4346                      | -19.9623                      | -20.6201                      | -20.0273                      | -27.9725                      | -20.8061                      | -21.2406                      | -21.0913                         | -20.9345                         | -21.3353                         |
+| 1.2305 | 4500 | 0.2401        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.3672 | 5000 | 0.2371        | 0.2373          | -21.9444                      | -19.3005                      | -19.2441                      | -17.7989                      | -19.0868                      | -20.3950                      | -19.1305                      | -21.5127                      | -20.6068                      | -19.3250                      | -21.5673                      | -22.8791                      | -29.3793                      | -19.4702                      | -18.8669                      | -19.2886                      | -19.8258                      | -19.3057                      | -20.0101                      | -19.3345                      | -27.5779                      | -20.1899                      | -20.6284                      | -20.5167                         | -20.3229                         | -20.7721                         |
+| 1.5040 | 5500 | 0.2349        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.6407 | 6000 | 0.2336        | 0.2346          | -21.6615                      | -18.9016                      | -18.8657                      | -17.3452                      | -18.6869                      | -20.0105                      | -18.7528                      | -21.1990                      | -20.2645                      | -18.9266                      | -21.2386                      | -22.6295                      | -29.2204                      | -19.0695                      | -18.4641                      | -18.9026                      | -19.4506                      | -18.9074                      | -19.6659                      | -18.9515                      | -27.3466                      | -19.8162                      | -20.2736                      | -20.1841                         | -19.9848                         | -20.4531                         |
+| 1.7774 | 6500 | 0.2319        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.9141 | 7000 | 0.2309        | 0.2332          | -21.5220                      | -18.7091                      | -18.6632                      | -17.1205                      | -18.4809                      | -19.8342                      | -18.5557                      | -21.0604                      | -20.0990                      | -18.7323                      | -21.0808                      | -22.4971                      | -29.1680                      | -18.8630                      | -18.2583                      | -18.6989                      | -19.2859                      | -18.7163                      | -19.4929                      | -18.7442                      | -27.2443                      | -19.6327                      | -20.1037                      | -20.0234                         | -19.8106                         | -20.3017                         |
+| 0.1367 | 500  | 0.2302        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.2734 | 1000 | 0.2261        | 0.2290          | -21.1100                      | -18.0936                      | -18.0277                      | -16.4059                      | -17.8516                      | -19.2687                      | -17.9684                      | -20.6744                      | -19.5689                      | -18.1063                      | -20.6725                      | -22.0790                      | -28.9503                      | -18.2049                      | -17.5842                      | -18.0814                      | -18.7115                      | -18.1111                      | -18.9581                      | -18.1032                      | -26.8510                      | -19.0325                      | -19.5538                      | -19.6006                         | -19.3362                         | -19.8807                         |
+| 0.4102 | 1500 | 0.222         | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.5469 | 2000 | 0.2188        | 0.2246          | -20.5835                      | -17.4530                      | -17.3853                      | -15.6663                      | -17.1929                      | -18.6930                      | -17.3208                      | -20.1688                      | -19.0165                      | -17.4784                      | -20.1460                      | -21.6056                      | -28.7345                      | -17.5632                      | -16.9100                      | -17.4263                      | -18.0993                      | -17.4835                      | -18.3902                      | -17.4462                      | -26.5854                      | -18.4647                      | -19.0091                      | -19.0492                         | -18.7904                         | -19.3776                         |
+| 0.6836 | 2500 | 0.2166        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.8203 | 3000 | 0.2148        | 0.2226          | -20.3772                      | -17.1675                      | -17.1095                      | -15.3337                      | -16.8981                      | -18.4286                      | -17.0421                      | -19.9421                      | -18.7571                      | -17.1871                      | -19.9155                      | -21.3992                      | -28.6587                      | -17.2521                      | -16.6051                      | -17.1500                      | -17.8465                      | -17.1935                      | -18.1342                      | -17.1320                      | -26.4308                      | -18.1833                      | -18.7496                      | -18.8118                         | -18.5408                         | -19.1404                         |
+| 0.9571 | 3500 | 0.2133        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cu121
+- 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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 +1,31 @@
+{
+  "_name_or_path": "google-bert/bert-base-multilingual-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.3",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2934204df31251acb1185c212142d11a0064ebc58f06444eae2a09fc0cad516c
+size 669448040

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}