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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:665
- 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: Is there a free return policy?
sentences:
- general query
- faq query
- product query
- source_sentence: Quiero reservar un vuelo a Madrid
sentences:
- faq query
- general query
- product query
- source_sentence: Bestell mir einen Bluetooth-Lautsprecher
sentences:
- faq query
- general query
- general query
- source_sentence: Kann ich meinen Account auf Premium upgraden?
sentences:
- faq query
- product query
- faq query
- source_sentence: Was kostet der Versand nach Kanada?
sentences:
- product query
- faq 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: 0.7060858093148971
name: Pearson Cosine
- type: spearman_cosine
value: 0.7122657953703283
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5850353380261794
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6010204119883696
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5997691394008732
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6079117189235353
name: Spearman Euclidean
- type: pearson_dot
value: 0.7251159526734934
name: Pearson Dot
- type: spearman_dot
value: 0.732939716175825
name: Spearman Dot
- type: pearson_max
value: 0.7251159526734934
name: Pearson Max
- type: spearman_max
value: 0.732939716175825
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: MiniLM test
type: MiniLM-test
metrics:
- type: pearson_cosine
value: 0.8232712880664017
name: Pearson Cosine
- type: spearman_cosine
value: 0.822196399839697
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7831863345453927
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8000293400400974
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.792921493930252
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.80506730817637
name: Spearman Euclidean
- type: pearson_dot
value: 0.8011854727667188
name: Pearson Dot
- type: spearman_dot
value: 0.8151432444489153
name: Spearman Dot
- type: pearson_max
value: 0.8232712880664017
name: Pearson Max
- type: spearman_max
value: 0.822196399839697
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 = [
'Was kostet der Versand nach Kanada?',
'faq query',
'product 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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### Downstream Usage (Sentence Transformers)
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<details><summary>Click to expand</summary>
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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 | 0.7061 |
| **spearman_cosine** | **0.7123** |
| pearson_manhattan | 0.585 |
| spearman_manhattan | 0.601 |
| pearson_euclidean | 0.5998 |
| spearman_euclidean | 0.6079 |
| pearson_dot | 0.7251 |
| spearman_dot | 0.7329 |
| pearson_max | 0.7251 |
| spearman_max | 0.7329 |
#### 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 | 0.8233 |
| **spearman_cosine** | **0.8222** |
| pearson_manhattan | 0.7832 |
| spearman_manhattan | 0.8 |
| pearson_euclidean | 0.7929 |
| spearman_euclidean | 0.8051 |
| pearson_dot | 0.8012 |
| spearman_dot | 0.8151 |
| pearson_max | 0.8233 |
| spearman_max | 0.8222 |
<!--
## 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.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 665 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: 7 tokens</li><li>mean: 11.29 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.31 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------|:---------------------------|:-----------------|
| <code>Send me deals on gaming accessories</code> | <code>product query</code> | <code>1.0</code> |
| <code>Aidez-moi à synchroniser mes contacts sur mon téléphone</code> | <code>faq query</code> | <code>0.0</code> |
| <code>Какие у вас есть предложения по ноутбукам?</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: 84 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: 7 tokens</li><li>mean: 11.32 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.42 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------------------------|:---------------------------|:-----------------|
| <code>كيف يمكنني تتبع شحنتي؟</code> | <code>support query</code> | <code>0.0</code> |
| <code>Aidez-moi à configurer une nouvelle adresse e-mail</code> | <code>support query</code> | <code>1.0</code> |
| <code>Envoyez-moi les dernières promotions sur les montres connectées</code> | <code>product 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
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 8
- `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`: 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
- `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`: 8
- `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 |
|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
| 0.4762 | 10 | 1.3639 | 0.8946 | 0.0665 | - |
| 0.9524 | 20 | 0.8488 | 0.7608 | 0.2318 | - |
| 1.4286 | 30 | 0.6629 | 1.0463 | 0.3736 | - |
| 1.9048 | 40 | 1.1413 | 1.1547 | 0.4159 | - |
| 2.3810 | 50 | 1.8156 | 1.2059 | 0.4760 | - |
| 2.8571 | 60 | 2.0179 | 0.8129 | 0.5794 | - |
| 3.3333 | 70 | 0.3202 | 0.6236 | 0.6217 | - |
| 3.8095 | 80 | 0.1437 | 0.6061 | 0.6404 | - |
| 4.2857 | 90 | 1.1623 | 0.7312 | 0.6424 | - |
| 4.7619 | 100 | 0.4376 | 0.5987 | 0.6621 | - |
| 5.2381 | 110 | 0.5832 | 0.4848 | 0.6837 | - |
| 5.7143 | 120 | 0.1749 | 0.3367 | 0.6896 | - |
| 6.1905 | 130 | 0.0192 | 0.2607 | 0.6936 | - |
| 6.6667 | 140 | 0.2047 | 0.2564 | 0.6995 | - |
| 7.1429 | 150 | 0.404 | 0.2747 | 0.7103 | - |
| 7.6190 | 160 | 0.0008 | 0.2764 | 0.7123 | - |
| 8.0 | 168 | - | - | - | 0.8222 |
### 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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