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-cased
+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-cased
+  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: -18.93259286880493
+      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: -15.68576693534851
+      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: -16.125640273094177
+      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: -13.388358056545258
+      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: -15.648126602172852
+      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: -17.174141108989716
+      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: -15.814268589019775
+      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: -18.483880162239075
+      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: -17.58536398410797
+      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: -15.706634521484375
+      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: -17.800691723823547
+      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: -19.26662176847458
+      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: -28.38749885559082
+      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: -15.783128142356873
+      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: -15.027229487895966
+      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: -15.598368644714355
+      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: -16.64138436317444
+      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: -15.76906442642212
+      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: -16.91163182258606
+      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: -15.555775165557861
+      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: -18.37025284767151
+      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: -16.945864260196686
+      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: -16.482776403427124
+      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: -16.996394097805023
+      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: -16.82070791721344
+      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: -17.381685972213745
+      name: Negative Mse
+---
+
+# SentenceTransformer based on google-bert/bert-base-multilingual-cased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). 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-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
+- **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-cased-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** | **-18.9326**     | **-15.6858**     | **-16.1256**     | **-13.3884**     | **-15.6481**     | **-17.1741**     | **-15.8143**     | **-18.4839**     | **-17.5854**     | **-15.7066**     | **-17.8007**     | **-19.2666**     | **-28.3875**     | **-15.7831**     | **-15.0272**     | **-15.5984**     | **-16.6414**     | **-15.7691**     | **-16.9116**     | **-15.5558**     | **-18.3703**     | **-16.9459**     | **-16.4828**     | **-16.9964**        | **-16.8207**        | **-17.3817**        |
+
+<!--
+## 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: 12.34 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.41 tokens</li><li>max: 49 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: 12.44 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.48 tokens</li><li>max: 49 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`: 4
+- `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`: 4
+- `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   | 0.3783        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.2734 | 1000  | 0.3256        | 0.3071          | -30.0050                      | -29.7152                      | -29.7584                      | -29.5204                      | -29.6875                      | -29.9032                      | -29.6918                      | -29.9795                      | -29.9430                      | -29.7142                      | -29.8220                      | -30.0745                      | -32.1218                      | -29.8042                      | -29.7132                      | -29.7625                      | -29.7677                      | -29.6658                      | -29.8250                      | -29.8242                      | -30.1233                      | -29.8640                      | -29.7497                      | -29.6833                         | -29.7296                         | -29.7063                         |
+| 0.4102 | 1500  | 0.3007        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.5469 | 2000  | 0.2795        | 0.2663          | -25.0193                      | -23.8364                      | -23.9924                      | -22.8145                      | -23.7158                      | -24.4490                      | -23.7719                      | -24.6885                      | -24.5973                      | -23.7662                      | -24.4998                      | -25.3625                      | -30.9153                      | -24.0474                      | -23.5674                      | -23.7934                      | -24.1332                      | -23.6279                      | -24.1308                      | -23.8860                      | -25.4166                      | -24.4840                      | -24.1931                      | -24.0816                         | -24.0634                         | -24.2529                         |
+| 0.6836 | 2500  | 0.2659        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 0.8203 | 3000  | 0.2562        | 0.2487          | -22.9862                      | -21.2544                      | -21.4573                      | -19.8714                      | -21.1251                      | -22.1884                      | -21.1984                      | -22.6963                      | -22.3069                      | -21.1959                      | -22.3180                      | -23.4410                      | -30.2373                      | -21.4324                      | -20.8799                      | -21.1834                      | -21.7427                      | -21.1291                      | -21.7291                      | -21.3003                      | -23.2994                      | -22.1537                      | -21.7480                      | -21.7521                         | -21.6844                         | -21.9702                         |
+| 0.9571 | 3500  | 0.2475        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.0938 | 4000  | 0.2411        | 0.2375          | -21.8220                      | -19.6064                      | -19.9128                      | -17.9872                      | -19.5372                      | -20.7666                      | -19.6563                      | -21.4985                      | -20.9295                      | -19.6182                      | -20.9963                      | -22.2441                      | -29.7291                      | -19.8001                      | -19.2003                      | -19.5189                      | -20.2697                      | -19.5946                      | -20.3160                      | -19.6652                      | -21.9553                      | -20.6678                      | -20.2305                      | -20.3719                         | -20.2700                         | -20.6528                         |
+| 1.2305 | 4500  | 0.2351        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.3672 | 5000  | 0.23          | 0.2296          | -21.0058                      | -18.4861                      | -18.7926                      | -16.6395                      | -18.4034                      | -19.7517                      | -18.5299                      | -20.6663                      | -19.9769                      | -18.4977                      | -20.0496                      | -21.4171                      | -29.3272                      | -18.6213                      | -17.9746                      | -18.3449                      | -19.2392                      | -18.4960                      | -19.3377                      | -18.5079                      | -20.9805                      | -19.5803                      | -19.1385                      | -19.4256                         | -19.2708                         | -19.7140                         |
+| 1.5040 | 5500  | 0.2257        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.6407 | 6000  | 0.2222        | 0.2245          | -20.4317                      | -17.7592                      | -18.1037                      | -15.7487                      | -17.6947                      | -19.0287                      | -17.8518                      | -20.1401                      | -19.3864                      | -17.7539                      | -19.4615                      | -20.8562                      | -29.1081                      | -17.8707                      | -17.1892                      | -17.6230                      | -18.5879                      | -17.7857                      | -18.7075                      | -17.7347                      | -20.2941                      | -18.8814                      | -18.4449                      | -18.8036                         | -18.6146                         | -19.1169                         |
+| 1.7774 | 6500  | 0.2186        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 1.9141 | 7000  | 0.2158        | 0.2199          | -19.9961                      | -17.0956                      | -17.4488                      | -14.9930                      | -17.0238                      | -18.4442                      | -17.1720                      | -19.6005                      | -18.7765                      | -17.1020                      | -18.8972                      | -20.3720                      | -28.8656                      | -17.1949                      | -16.4824                      | -16.9655                      | -17.9687                      | -17.1229                      | -18.0911                      | -17.0128                      | -19.6600                      | -18.2823                      | -17.8109                      | -18.2341                         | -18.0582                         | -18.5735                         |
+| 2.0509 | 7500  | 0.2135        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 2.1876 | 8000  | 0.2109        | 0.2167          | -19.6376                      | -16.6362                      | -17.0307                      | -14.4461                      | -16.5766                      | -18.0419                      | -16.7080                      | -19.2403                      | -18.3971                      | -16.6443                      | -18.5251                      | -20.0263                      | -28.7414                      | -16.7279                      | -15.9992                      | -16.5092                      | -17.5170                      | -16.6766                      | -17.7151                      | -16.5403                      | -19.2861                      | -17.8316                      | -17.3764                      | -17.8453                         | -17.6606                         | -18.1844                         |
+| 2.3243 | 8500  | 0.2088        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 2.4610 | 9000  | 0.2074        | 0.2149          | -19.4358                      | -16.3728                      | -16.7740                      | -14.1447                      | -16.3289                      | -17.8191                      | -16.4582                      | -19.0369                      | -18.1738                      | -16.3903                      | -18.3565                      | -19.8207                      | -28.6133                      | -16.4804                      | -15.7354                      | -16.2673                      | -17.3034                      | -16.4190                      | -17.4826                      | -16.2566                      | -18.9971                      | -17.5950                      | -17.1273                      | -17.6066                         | -17.4124                         | -17.9799                         |
+| 2.5978 | 9500  | 0.2059        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 2.7345 | 10000 | 0.2047        | 0.2134          | -19.2764                      | -16.1718                      | -16.5449                      | -13.8928                      | -16.1098                      | -17.5866                      | -16.2421                      | -18.8665                      | -17.9798                      | -16.1538                      | -18.1695                      | -19.6218                      | -28.5605                      | -16.2479                      | -15.4962                      | -16.0522                      | -17.0797                      | -16.2106                      | -17.3130                      | -16.0278                      | -18.8206                      | -17.3910                      | -16.9231                      | -17.4203                         | -17.2266                         | -17.7903                         |
+| 2.8712 | 10500 | 0.2033        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 3.0079 | 11000 | 0.2024        | 0.2120          | -19.1026                      | -15.9149                      | -16.3497                      | -13.6750                      | -15.8828                      | -17.3842                      | -16.0397                      | -18.6612                      | -17.7796                      | -15.9436                      | -17.9779                      | -19.4370                      | -28.4678                      | -16.0245                      | -15.2818                      | -15.8265                      | -16.8594                      | -15.9988                      | -17.1163                      | -15.8106                      | -18.5870                      | -17.1548                      | -16.7074                      | -17.2082                         | -17.0233                         | -17.5910                         |
+| 3.1447 | 11500 | 0.201         | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 3.2814 | 12000 | 0.2004        | 0.2112          | -19.0406                      | -15.8196                      | -16.2516                      | -13.5420                      | -15.7688                      | -17.2734                      | -15.9280                      | -18.5894                      | -17.6966                      | -15.8265                      | -17.8933                      | -19.3785                      | -28.4539                      | -15.9129                      | -15.1631                      | -15.7175                      | -16.7540                      | -15.8974                      | -17.0251                      | -15.6875                      | -18.4807                      | -17.0615                      | -16.6087                      | -17.1051                         | -16.9423                         | -17.4923                         |
+| 3.4181 | 12500 | 0.1997        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 3.5548 | 13000 | 0.1995        | 0.2108          | -18.9779                      | -15.7524                      | -16.1996                      | -13.4723                      | -15.7211                      | -17.2272                      | -15.8790                      | -18.5412                      | -17.6416                      | -15.7862                      | -17.8502                      | -19.3124                      | -28.4179                      | -15.8513                      | -15.1030                      | -15.6645                      | -16.7053                      | -15.8355                      | -16.9742                      | -15.6246                      | -18.4384                      | -17.0053                      | -16.5478                      | -17.0674                         | -16.8851                         | -17.4527                         |
+| 3.6916 | 13500 | 0.1991        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+| 3.8283 | 14000 | 0.1987        | 0.2103          | -18.9326                      | -15.6858                      | -16.1256                      | -13.3884                      | -15.6481                      | -17.1741                      | -15.8143                      | -18.4839                      | -17.5854                      | -15.7066                      | -17.8007                      | -19.2666                      | -28.3875                      | -15.7831                      | -15.0272                      | -15.5984                      | -16.6414                      | -15.7691                      | -16.9116                      | -15.5558                      | -18.3703                      | -16.9459                      | -16.4828                      | -16.9964                         | -16.8207                         | -17.3817                         |
+| 3.9650 | 14500 | 0.1989        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
+
+
+### 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-cased",
+  "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": 119547
+}

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:be0cb3f9cbb2f431475c9103176ad929e674f091dd57253d07184c5d77801973
+size 711436136

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": false,
+  "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]"
+}