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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1267
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Give me suggestions for a high-quality DSLR camera
sentences:
- faq query
- subscription query
- faq query
- source_sentence: Aidez-moi à configurer une nouvelle adresse e-mail
sentences:
- order query
- faq query
- feedback query
- source_sentence: Как я могу изменить адрес доставки?
sentences:
- support query
- product query
- product query
- source_sentence: ساعدني في حذف الملفات الغير مرغوب فيها من هاتفي
sentences:
- technical support query
- product recommendation
- faq query
- source_sentence: Envoyez-moi la politique de garantie de ce produit
sentences:
- faq query
- account query
- faq query
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: MiniLM dev
type: MiniLM-dev
metrics:
- type: pearson_cosine
value: 0.6538226572138826
name: Pearson Cosine
- type: spearman_cosine
value: 0.6336766646599241
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5799895241429639
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5525776786782183
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5732001104236694
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5394971970682657
name: Spearman Euclidean
- type: pearson_dot
value: 0.6359725423136287
name: Pearson Dot
- type: spearman_dot
value: 0.6237936341101822
name: Spearman Dot
- type: pearson_max
value: 0.6538226572138826
name: Pearson Max
- type: spearman_max
value: 0.6336766646599241
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: MiniLM test
type: MiniLM-test
metrics:
- type: pearson_cosine
value: 0.6682368113711722
name: Pearson Cosine
- type: spearman_cosine
value: 0.6222011918428743
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5714617063306076
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5481366191719228
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5726946277850402
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.549312247309557
name: Spearman Euclidean
- type: pearson_dot
value: 0.6396412507506479
name: Pearson Dot
- type: spearman_dot
value: 0.6107388175009413
name: Spearman Dot
- type: pearson_max
value: 0.6682368113711722
name: Pearson Max
- type: spearman_max
value: 0.6222011918428743
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). 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:** 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': 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("philipp-zettl/MiniLM-similarity-small")
# Run inference
sentences = [
'Envoyez-moi la politique de garantie de ce produit',
'faq query',
'account query',
]
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]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `MiniLM-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6538 |
| **spearman_cosine** | **0.6337** |
| pearson_manhattan | 0.58 |
| spearman_manhattan | 0.5526 |
| pearson_euclidean | 0.5732 |
| spearman_euclidean | 0.5395 |
| pearson_dot | 0.636 |
| spearman_dot | 0.6238 |
| pearson_max | 0.6538 |
| spearman_max | 0.6337 |
#### Semantic Similarity
* Dataset: `MiniLM-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6682 |
| **spearman_cosine** | **0.6222** |
| pearson_manhattan | 0.5715 |
| spearman_manhattan | 0.5481 |
| pearson_euclidean | 0.5727 |
| spearman_euclidean | 0.5493 |
| pearson_dot | 0.6396 |
| spearman_dot | 0.6107 |
| pearson_max | 0.6682 |
| spearman_max | 0.6222 |
<!--
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,267 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.77 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.31 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.67</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------|:---------------------------|:-----------------|
| <code>Get information on the next art exhibition</code> | <code>product query</code> | <code>0.0</code> |
| <code>Show me how to update my profile</code> | <code>product query</code> | <code>0.0</code> |
| <code>Покажите мне доступные варианты полетов в Турцию</code> | <code>faq query</code> | <code>0.0</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
#### Unnamed Dataset
* Size: 159 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.65 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.35 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.67</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------|:---------------------------|:-----------------|
| <code>Sende mir die Bestellbestätigung per E-Mail</code> | <code>order query</code> | <code>0.0</code> |
| <code>How do I add a new payment method?</code> | <code>faq query</code> | <code>1.0</code> |
| <code>No puedo conectar mi impresora, ¿puedes ayudarme?</code> | <code>support query</code> | <code>1.0</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`: steps
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 2
- `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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
| 0.0629 | 10 | 6.2479 | 2.5890 | 0.1448 | - |
| 0.1258 | 20 | 4.3549 | 2.2787 | 0.1965 | - |
| 0.1887 | 30 | 3.5969 | 2.0104 | 0.2599 | - |
| 0.2516 | 40 | 2.4979 | 1.7269 | 0.3357 | - |
| 0.3145 | 50 | 2.5551 | 1.5747 | 0.4439 | - |
| 0.3774 | 60 | 3.1446 | 1.4892 | 0.4750 | - |
| 0.4403 | 70 | 2.1353 | 1.5305 | 0.4662 | - |
| 0.5031 | 80 | 2.9341 | 1.3718 | 0.4848 | - |
| 0.5660 | 90 | 2.8709 | 1.2469 | 0.5316 | - |
| 0.6289 | 100 | 2.1367 | 1.2558 | 0.5436 | - |
| 0.6918 | 110 | 2.2735 | 1.2939 | 0.5392 | - |
| 0.7547 | 120 | 2.8646 | 1.1206 | 0.5616 | - |
| 0.8176 | 130 | 3.3204 | 1.0213 | 0.5662 | - |
| 0.8805 | 140 | 0.8989 | 0.9866 | 0.5738 | - |
| 0.9434 | 150 | 0.0057 | 0.9961 | 0.5674 | - |
| 1.0063 | 160 | 0.0019 | 1.0111 | 0.5674 | - |
| 1.0692 | 170 | 0.4617 | 1.0275 | 0.5747 | - |
| 1.1321 | 180 | 0.0083 | 1.0746 | 0.5732 | - |
| 1.1950 | 190 | 0.5048 | 1.0968 | 0.5753 | - |
| 1.2579 | 200 | 0.0002 | 1.0840 | 0.5738 | - |
| 1.3208 | 210 | 0.07 | 1.0364 | 0.5753 | - |
| 1.3836 | 220 | 0.0 | 0.9952 | 0.5750 | - |
| 1.4465 | 230 | 0.0 | 0.9922 | 0.5744 | - |
| 1.5094 | 240 | 0.0 | 0.9923 | 0.5726 | - |
| 1.0126 | 250 | 0.229 | 0.9930 | 0.5729 | - |
| 1.0755 | 260 | 2.2061 | 0.9435 | 0.5880 | - |
| 1.1384 | 270 | 2.7711 | 0.8892 | 0.6078 | - |
| 1.2013 | 280 | 0.7528 | 0.8886 | 0.6148 | - |
| 1.2642 | 290 | 0.386 | 0.8927 | 0.6162 | - |
| 1.3270 | 300 | 0.8902 | 0.8710 | 0.6267 | - |
| 1.3899 | 310 | 0.9534 | 0.8429 | 0.6337 | - |
| 1.4403 | 318 | - | - | - | 0.6222 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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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