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---
language:
- multilingual
- zh
- ja
- ar
- ko
- de
- fr
- es
- pt
- hi
- id
- it
- tr
- ru
- bn
- ur
- mr
- ta
- vi
- fa
- pl
- uk
- nl
- sv
- he
- sw
- ps
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Is that wrong?
sentences:
- Is that such a terrible thing?
- Kennedy korkunç bir savcıydı.
- Tom bir davada tanıklık ediyordu.
- source_sentence: Orada mıydılar?
sentences:
- Were they in there?
- İlki ikincisini anlamlı kılar.
- Alerji tedavisi gelişiyor.
- source_sentence: He is not alone
sentences:
- It is not confusing
- The Hawks were humanitarians.
- Tom bir davada tanıklık ediyordu.
- source_sentence: Yaptığın şey bu.
sentences:
- Onurlu işler yapıyorsunuz.
- Weisberg azınlık adına konuştu.
- Robert Ferrigno Kaliforniya'da doğdu.
- source_sentence: Ben vatansızım.
sentences:
- I am stateless.
- Kendi tekniğini tercih ediyor.
- Mermiler camdan fırladı.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: tr ling
type: tr_ling
metrics:
- type: pearson_cosine
value: 0.037604255015168134
name: Pearson Cosine
- type: spearman_cosine
value: 0.04804112988506346
name: Spearman Cosine
- type: pearson_manhattan
value: 0.034740275152181296
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.03769766156967754
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.03698411306484619
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.03903062430281842
name: Spearman Euclidean
- type: pearson_dot
value: 0.0673696846368413
name: Pearson Dot
- type: spearman_dot
value: 0.06818119362900125
name: Spearman Dot
- type: pearson_max
value: 0.0673696846368413
name: Pearson Max
- type: spearman_max
value: 0.06818119362900125
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) dataset. It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7)
- **Languages:** multilingual, zh, ja, ar, ko, de, fr, es, pt, hi, id, it, tr, ru, bn, ur, mr, ta, vi, fa, pl, uk, nl, sv, he, sw, ps
<!-- - **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': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Ben vatansızım.',
'I am stateless.',
'Kendi tekniğini tercih ediyor.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Semantic Similarity
* Dataset: `tr_ling`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.0376 |
| spearman_cosine | 0.048 |
| pearson_manhattan | 0.0347 |
| spearman_manhattan | 0.0377 |
| pearson_euclidean | 0.037 |
| spearman_euclidean | 0.039 |
| pearson_dot | 0.0674 |
| spearman_dot | 0.0682 |
| pearson_max | 0.0674 |
| **spearman_max** | **0.0682** |
<!--
## 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
#### MoritzLaurer/multilingual-nli-26lang-2mil7
* Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538)
* Size: 25,000 training samples
* Columns: <code>premise_original</code>, <code>hypothesis_original</code>, <code>score</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | premise_original | hypothesis_original | score | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | int | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 29.3 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.62 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>0: ~34.50%</li><li>1: ~33.30%</li><li>2: ~32.20%</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 28.28 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.39 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
| premise_original | hypothesis_original | score | sentence1 | sentence2 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>N, the total number of LC50 values used in calculating the CV(%) varied with organism and toxicant because some data were rejected due to water hardness, lack of concentration measurements, and/or because some of the LC50s were not calculable.</code> | <code>Most discarded data was rejected due to water hardness.</code> | <code>1</code> | <code>N, CV'nin hesaplanmasında kullanılan LC50 değerlerinin toplam sayısı (%) organizma ve toksik madde ile çeşitlidir, çünkü bazı veriler su sertliği, konsantrasyon ölçümlerinin eksikliği ve / veya LC50'lerin bazıları hesaplanamaz olduğu için reddedilmiştir.</code> | <code>Atılan verilerin çoğu su sertliği nedeniyle reddedildi.</code> |
| <code>As the home of the Venus de Milo and Mona Lisa, the Louvre drew almost unmanageable crowds until President Mitterrand ordered its re-organization in the 1980s.</code> | <code>The Louvre is home of the Venus de Milo and Mona Lisa.</code> | <code>0</code> | <code>Venus de Milo ve Mona Lisa'nın evi olarak Louvre, Başkan Mitterrand'ın 1980'lerde yeniden düzenlenmesini emredene kadar neredeyse yönetilemez kalabalıklar çekti.</code> | <code>Louvre, Venus de Milo ve Mona Lisa'nın evidir.</code> |
| <code>A year ago, the wife of the Oxford don noticed that the pattern on Kleenex quilted tissue uncannily resembled the Penrose Arrowed Rhombi tilings pattern, which Sir Roger had invented--and copyrighted--in 1974.</code> | <code>It has been recently found out a similarity between the pattern on the recent Kleenex quilted tissue and the one of the Penrose Arrowed Rhombi tilings.</code> | <code>0</code> | <code>Bir yıl önce Oxford'un karısı, Kleenex kapitone dokudaki desenin 1974'te Sir Roger'ın icat ettiği -ve telif hakkı olan - Penrose Arrowed Rhombi tilings desenine benzediğini fark etti.</code> | <code>Yakın zamanda, son Kleenex kapitone dokudaki desen ile Penrose Arrowed Rhombi döşemelerinden biri arasında bir benzerlik bulunmuştur.</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### MoritzLaurer/multilingual-nli-26lang-2mil7
* Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538)
* Size: 5,000 evaluation samples
* Columns: <code>premise_original</code>, <code>hypothesis_original</code>, <code>score</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | premise_original | hypothesis_original | score | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | int | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 30.3 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~34.50%</li><li>1: ~29.90%</li><li>2: ~35.60%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 29.94 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.29 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
| premise_original | hypothesis_original | score | sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------|:------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| <code>But the racism charge isn't quirky or wacky--it's demagogy.</code> | <code>The accusation of prejudice based on a pedestrian kind of hatred.</code> | <code>0</code> | <code>Ama ırkçılık suçlaması tuhaf ya da tuhaf değil, bu bir demagoji.</code> | <code>Yaya nefretine dayanan önyargı suçlaması.</code> |
| <code>Why would Gates allow the publication of such a book with his byline and photo on the dust jacket?</code> | <code>Gates' byline and photo are on the dust jacket</code> | <code>0</code> | <code>Gates neden böyle bir kitabın basılmasına izin versin ki?</code> | <code>Gates'in çizgisi ve fotoğrafı toz ceketin üzerinde.</code> |
| <code>I am a nonsmoker and allergic to cigarette smoke.</code> | <code>I do not smoke.</code> | <code>0</code> | <code>Sigara içmeyen biriyim ve sigara dumanına alerjim var.</code> | <code>Sigara içmiyorum.</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `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
- `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`: 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`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `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
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | tr_ling_spearman_max |
|:------:|:----:|:-------------:|:------:|:--------------------:|
| 0.0320 | 25 | 17.17 | - | - |
| 0.0639 | 50 | 16.4932 | - | - |
| 0.0959 | 75 | 16.5976 | - | - |
| 0.1279 | 100 | 15.6991 | - | - |
| 0.1598 | 125 | 14.876 | - | - |
| 0.1918 | 150 | 14.4828 | - | - |
| 0.2238 | 175 | 12.7061 | - | - |
| 0.2558 | 200 | 10.8687 | - | - |
| 0.2877 | 225 | 8.3797 | - | - |
| 0.3197 | 250 | 6.2029 | - | - |
| 0.3517 | 275 | 5.8228 | - | - |
| 0.3836 | 300 | 5.811 | - | - |
| 0.4156 | 325 | 5.8079 | - | - |
| 0.4476 | 350 | 5.8077 | - | - |
| 0.4795 | 375 | 5.8035 | - | - |
| 0.5115 | 400 | 5.8072 | - | - |
| 0.5435 | 425 | 5.8033 | - | - |
| 0.5754 | 450 | 5.8086 | - | - |
| 0.6074 | 475 | 5.81 | - | - |
| 0.6394 | 500 | 5.7949 | - | - |
| 0.6714 | 525 | 5.8079 | - | - |
| 0.7033 | 550 | 5.8057 | - | - |
| 0.7353 | 575 | 5.8097 | - | - |
| 0.7673 | 600 | 5.7986 | - | - |
| 0.7992 | 625 | 5.8051 | - | - |
| 0.8312 | 650 | 5.8041 | - | - |
| 0.8632 | 675 | 5.7907 | - | - |
| 0.8951 | 700 | 5.7991 | - | - |
| 0.9271 | 725 | 5.8035 | - | - |
| 0.9591 | 750 | 5.7945 | - | - |
| 0.9910 | 775 | 5.8077 | - | - |
| 1.0 | 782 | - | 5.8024 | 0.0330 |
| 1.0230 | 800 | 5.6703 | - | - |
| 1.0550 | 825 | 5.8052 | - | - |
| 1.0870 | 850 | 5.7936 | - | - |
| 1.1189 | 875 | 5.7924 | - | - |
| 1.1509 | 900 | 5.7806 | - | - |
| 1.1829 | 925 | 5.7835 | - | - |
| 1.2148 | 950 | 5.7619 | - | - |
| 1.2468 | 975 | 5.8038 | - | - |
| 1.2788 | 1000 | 5.779 | - | - |
| 1.3107 | 1025 | 5.7904 | - | - |
| 1.3427 | 1050 | 5.7696 | - | - |
| 1.3747 | 1075 | 5.7919 | - | - |
| 1.4066 | 1100 | 5.7785 | - | - |
| 1.4386 | 1125 | 5.7862 | - | - |
| 1.4706 | 1150 | 5.7703 | - | - |
| 1.5026 | 1175 | 5.773 | - | - |
| 1.5345 | 1200 | 5.7627 | - | - |
| 1.5665 | 1225 | 5.7596 | - | - |
| 1.5985 | 1250 | 5.7882 | - | - |
| 1.6304 | 1275 | 5.7828 | - | - |
| 1.6624 | 1300 | 5.771 | - | - |
| 1.6944 | 1325 | 5.788 | - | - |
| 1.7263 | 1350 | 5.7719 | - | - |
| 1.7583 | 1375 | 5.7846 | - | - |
| 1.7903 | 1400 | 5.7838 | - | - |
| 1.8223 | 1425 | 5.7912 | - | - |
| 1.8542 | 1450 | 5.7686 | - | - |
| 1.8862 | 1475 | 5.7938 | - | - |
| 1.9182 | 1500 | 5.7847 | - | - |
| 1.9501 | 1525 | 5.7952 | - | - |
| 1.9821 | 1550 | 5.7528 | - | - |
| 2.0 | 1564 | - | 5.7933 | 0.0682 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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