sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:40000 loss:MSELoss arxiv:1908.10084 arxiv:2004.09813 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:40000
- loss:MSELoss
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.
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]
```

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## 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** |

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## 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",
}
```

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