initial commit

.gitattributes

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

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 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:40000
+- loss:MSELoss
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+widget:
+- source_sentence: Who is filming along?
+  sentences:
+  - Wién filmt mat?
+  - Weider huet den Tatarescu drop higewisen, datt Rumänien durch seng krichsbedélegong
+    op de 6eite vun den allie'erten 110.000 mann verluer hätt.
+  - Brambilla 130.08.03 St.
+- source_sentence: 'Four potential scenarios could still play out: Jean Asselborn.'
+  sentences:
+  - Dann ass nach eng Antenne hei um Kierchbierg virgesi Richtung RTL Gebai, do gëtt
+    jo een ganz neie Wunnquartier gebaut.
+  - D'bedélegong un de wählen wir ganz stärk gewiéscht a munche ge'genden wor re eso'gucr
+    me' we' 90 prozent.
+  - Jean Asselborn gesäit 4 Méiglechkeeten, wéi et kéint virugoen.
+- source_sentence: Non-profit organisation Passerell, which provides legal council
+    to refugees in Luxembourg, announced that it has to make four employees redundant
+    in August due to a lack of funding.
+  sentences:
+  - Oetringen nach Remich....8.20» 215»
+  - D'ASBL Passerell, déi sech ëm d'Berodung vu Refugiéeën a Saache Rechtsfroe këmmert,
+    wäert am August mussen hir véier fix Salariéen entloossen.
+  - D'Regierung huet allerdéngs "just" 180.041 Doudeger verzeechent.
+- source_sentence: This regulation was temporarily lifted during the Covid pandemic.
+  sentences:
+  - Six Jours vu New-York si fir d’équipe Girgetti — Debacco
+  - Dës Reegelung gouf wärend der Covid-Pandemie ausgesat.
+  - ING-Marathon ouni gréisser Tëschefäll ofgelaf - 18 Leit hospitaliséiert.
+- source_sentence: The cross-border workers should also receive more wages.
+  sentences:
+  - D'grenzarbechetr missten och me' lo'n kre'en.
+  - 'De Néckel: Firun! Dât ass jo ailes, wèll ''t get dach neischt un der Bréck gemâcht!'
+  - D'Grande-Duchesse Josephine Charlotte an hir Ministeren hunn d'Land verlooss,
+    et war den Optakt vun der Zäit am Exil.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- negative_mse
+- src2trg_accuracy
+- trg2src_accuracy
+- mean_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+  results:
+  - task:
+      type: knowledge-distillation
+      name: Knowledge Distillation
+    dataset:
+      name: lb en
+      type: lb-en
+    metrics:
+    - type: negative_mse
+      value: -0.47610557079315186
+      name: Negative Mse
+  - task:
+      type: translation
+      name: Translation
+    dataset:
+      name: lb en
+      type: lb-en
+    metrics:
+    - type: src2trg_accuracy
+      value: 0.9861111111111112
+      name: Src2Trg Accuracy
+    - type: trg2src_accuracy
+      value: 0.9861111111111112
+      name: Trg2Src Accuracy
+    - type: mean_accuracy
+      value: 0.9861111111111112
+      name: Mean Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the lb-en dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - lb-en
+<!-- - **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: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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("aloizidis/make-multilingual-en-lb-2025-02-28_01-09-55")
+# Run inference
+sentences = [
+    'The cross-border workers should also receive more wages.',
+    "D'grenzarbechetr missten och me' lo'n kre'en.",
+    "De Néckel: Firun! Dât ass jo ailes, wèll 't get dach neischt un der Bréck gemâcht!",
+]
+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
+
+* Dataset: `lb-en`
+* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
+
+| Metric           | Value       |
+|:-----------------|:------------|
+| **negative_mse** | **-0.4761** |
+
+#### Translation
+
+* Dataset: `lb-en`
+* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
+
+| Metric            | Value      |
+|:------------------|:-----------|
+| src2trg_accuracy  | 0.9861     |
+| trg2src_accuracy  | 0.9861     |
+| **mean_accuracy** | **0.9861** |
+
+<!--
+## 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
+
+#### lb-en
+
+* Dataset: lb-en
+* Size: 40,000 training samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | english                                                                            | non_english                                                                        | label                                |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                             | string                                                                             | list                                 |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 25.32 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.91 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                                                                                          | non_english                                                                                                                         | label                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
+  | <code>A lesson for the next year</code>                                                                                          | <code>Eng le’er fir dat anert joer</code>                                                                                           | <code>[0.08891881257295609, 0.20895496010780334, -0.10672671347856522, -0.03302554786205292, 0.049002278596162796, ...]</code>    |
+  | <code>On Easter, the Maquisards' northern section organizes their big spring ball in Willy Pintsch's hall at the station.</code> | <code>Op O'schteren organisieren d'Maquisard'eiii section Nord, hire gro'sse fre'joersbal am sali Willy Pintsch op der gare.</code> | <code>[-0.08668982982635498, -0.06969941407442093, -0.0036096556577831507, 0.1605304628610611, -0.041704729199409485, ...]</code> |
+  | <code>The happiness, the peace is long gone now,</code>                                                                          | <code>V ergângen ass nu läng dat gléck, de' fréd,</code>                                                                            | <code>[0.07229219377040863, 0.3288629353046417, -0.012548360042273998, 0.06720984727144241, -0.02617395855486393, ...]</code>     |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Evaluation Dataset
+
+#### lb-en
+
+* Dataset: lb-en
+* Size: 504 evaluation samples
+* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
+* Approximate statistics based on the first 504 samples:
+  |         | english                                                                           | non_english                                                                        | label                                |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                            | string                                                                             | list                                 |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 25.23 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 36.48 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
+* Samples:
+  | english                                                                                                                                               | non_english                                                                                                                                                | label                                                                                                                          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
+  | <code>But he was not the instigator of the mass murders of the Jews, his lawyer explained, and he bore no more responsibility than the others.</code> | <code>Mé hié wir net den ustêfter vun de massemuerden un de judden, erklärt sein affekot, an hicn hätt net me' verantwortong ze droen we' de' aner.</code> | <code>[0.021159790456295013, 0.11144042760133743, 0.00869293138384819, 0.004551620222628117, -0.09236127883195877, ...]</code> |
+  | <code>The Romanian automotive industry * For the first time in its history, Romania has started car production.</code>                                | <code>D’rumänesch autoindustrie * Fir d'c'schte ke'er an senger geschieht huet Rumänien d'fabrikalio'n vun'den autoen opgeholl.</code>                     | <code>[-0.16835248470306396, 0.14826826751232147, 0.01772368885576725, -0.027855699881911278, 0.04770198464393616, ...]</code> |
+  | <code>The drugs were confiscated along with the dealer's car, mobile phones and cash.</code>                                                          | <code>D'Drogen, den Auto, d'Boergeld an d'Handye si saiséiert ginn.</code>                                                                                 | <code>[-0.05122023820877075, 0.01204440463334322, -0.025424882769584656, 0.1286350041627884, 0.034633491188287735, ...]</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`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `bf16`: 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`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: 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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `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 | lb-en loss | lb-en_negative_mse | lb-en_mean_accuracy |
+|:------:|:----:|:-------------:|:----------:|:------------------:|:-------------------:|
+| 0.08   | 100  | 0.0056        | 0.0048     | -0.7796            | 0.7887              |
+| 0.16   | 200  | 0.0051        | 0.0046     | -0.7330            | 0.8373              |
+| 0.24   | 300  | 0.0049        | 0.0044     | -0.6992            | 0.8740              |
+| 0.32   | 400  | 0.0047        | 0.0043     | -0.6763            | 0.8889              |
+| 0.4    | 500  | 0.0046        | 0.0042     | -0.6584            | 0.8988              |
+| 0.48   | 600  | 0.0045        | 0.0041     | -0.6377            | 0.9067              |
+| 0.56   | 700  | 0.0044        | 0.0040     | -0.6209            | 0.9206              |
+| 0.64   | 800  | 0.0043        | 0.0040     | -0.6087            | 0.9266              |
+| 0.72   | 900  | 0.0043        | 0.0039     | -0.5984            | 0.9395              |
+| 0.8    | 1000 | 0.0042        | 0.0038     | -0.5887            | 0.9385              |
+| 0.88   | 1100 | 0.0042        | 0.0038     | -0.5799            | 0.9425              |
+| 0.96   | 1200 | 0.0041        | 0.0038     | -0.5725            | 0.9474              |
+| 1.04   | 1300 | 0.004         | 0.0037     | -0.5690            | 0.9524              |
+| 1.12   | 1400 | 0.0039        | 0.0037     | -0.5602            | 0.9554              |
+| 1.2    | 1500 | 0.0038        | 0.0037     | -0.5545            | 0.9603              |
+| 1.28   | 1600 | 0.0038        | 0.0036     | -0.5501            | 0.9673              |
+| 1.3600 | 1700 | 0.0038        | 0.0036     | -0.5459            | 0.9643              |
+| 1.44   | 1800 | 0.0037        | 0.0036     | -0.5411            | 0.9702              |
+| 1.52   | 1900 | 0.0038        | 0.0036     | -0.5360            | 0.9722              |
+| 1.6    | 2000 | 0.0037        | 0.0035     | -0.5326            | 0.9683              |
+| 1.6800 | 2100 | 0.0037        | 0.0035     | -0.5310            | 0.9732              |
+| 1.76   | 2200 | 0.0036        | 0.0035     | -0.5264            | 0.9752              |
+| 1.8400 | 2300 | 0.0037        | 0.0035     | -0.5224            | 0.9792              |
+| 1.92   | 2400 | 0.0036        | 0.0035     | -0.5205            | 0.9792              |
+| 2.0    | 2500 | 0.0036        | 0.0034     | -0.5166            | 0.9782              |
+| 2.08   | 2600 | 0.0033        | 0.0034     | -0.5137            | 0.9782              |
+| 2.16   | 2700 | 0.0034        | 0.0034     | -0.5121            | 0.9812              |
+| 2.24   | 2800 | 0.0033        | 0.0034     | -0.5093            | 0.9802              |
+| 2.32   | 2900 | 0.0034        | 0.0034     | -0.5063            | 0.9821              |
+| 2.4    | 3000 | 0.0034        | 0.0034     | -0.5051            | 0.9802              |
+| 2.48   | 3100 | 0.0034        | 0.0034     | -0.5030            | 0.9812              |
+| 2.56   | 3200 | 0.0033        | 0.0033     | -0.5002            | 0.9851              |
+| 2.64   | 3300 | 0.0034        | 0.0033     | -0.4962            | 0.9831              |
+| 2.7200 | 3400 | 0.0034        | 0.0033     | -0.4936            | 0.9831              |
+| 2.8    | 3500 | 0.0033        | 0.0033     | -0.4916            | 0.9841              |
+| 2.88   | 3600 | 0.0033        | 0.0033     | -0.4892            | 0.9841              |
+| 2.96   | 3700 | 0.0033        | 0.0033     | -0.4871            | 0.9841              |
+| 3.04   | 3800 | 0.0032        | 0.0033     | -0.4863            | 0.9861              |
+| 3.12   | 3900 | 0.0031        | 0.0033     | -0.4864            | 0.9841              |
+| 3.2    | 4000 | 0.0031        | 0.0033     | -0.4859            | 0.9841              |
+| 3.2800 | 4100 | 0.0031        | 0.0033     | -0.4848            | 0.9871              |
+| 3.36   | 4200 | 0.0031        | 0.0033     | -0.4838            | 0.9881              |
+| 3.44   | 4300 | 0.0031        | 0.0032     | -0.4837            | 0.9861              |
+| 3.52   | 4400 | 0.0031        | 0.0032     | -0.4817            | 0.9851              |
+| 3.6    | 4500 | 0.0031        | 0.0032     | -0.4812            | 0.9841              |
+| 3.68   | 4600 | 0.0031        | 0.0032     | -0.4792            | 0.9861              |
+| 3.76   | 4700 | 0.0031        | 0.0032     | -0.4793            | 0.9851              |
+| 3.84   | 4800 | 0.0031        | 0.0032     | -0.4779            | 0.9871              |
+| 3.92   | 4900 | 0.0031        | 0.0032     | -0.4771            | 0.9861              |
+| 4.0    | 5000 | 0.0031        | 0.0032     | -0.4761            | 0.9861              |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.6.0
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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

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+{
+  "_name_or_path": "./output/make-multilingual-en-lb-2025-02-28_01-09-55/checkpoint-5000",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.6.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 1112197096

modules.json

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

sentence_bert_config.json

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

special_tokens_map.json

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+{
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+    "lstrip": false,
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+}

tokenizer.json

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+size 17082987

tokenizer_config.json

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+}