README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:844
- 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: Hilf mir, das Software-Update durchzuführen
  sentences:
  - order query
  - support query
  - faq query
- source_sentence: 马上给我提供这个商品的跟踪信息
  sentences:
  - payment query
  - technical support query
  - support query
- source_sentence: Downgrade my subscription plan
  sentences:
  - support query
  - product query
  - product query
- source_sentence: Help resolve issues with my operating system
  sentences:
  - technical support query
  - product query
  - product query
- source_sentence: Ayúdame a solucionar problemas de red
  sentences:
  - product query
  - support query
  - product 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: 0.7960441122484267
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8189711310679958
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6824455970208276
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.701004701178111
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6821384996384094
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7065633287645454
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7871337514786776
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7979718712970215
      name: Spearman Dot
    - type: pearson_max
      value: 0.7960441122484267
      name: Pearson Max
    - type: spearman_max
      value: 0.8189711310679958
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: MiniLM test
      type: MiniLM-test
    metrics:
    - type: pearson_cosine
      value: 0.7614418952584415
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7585961676423125
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.620319727073133
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6192118311486844
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6116132687052156
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6124276377795256
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7670292333817905
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7764817683428225
      name: Spearman Dot
    - type: pearson_max
      value: 0.7670292333817905
      name: Pearson Max
    - type: spearman_max
      value: 0.7764817683428225
      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 = [
    'Ayúdame a solucionar problemas de red',
    'support 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]
```

<!--
### 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: `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      | 0.796     |
| **spearman_cosine** | **0.819** |
| pearson_manhattan   | 0.6824    |
| spearman_manhattan  | 0.701     |
| pearson_euclidean   | 0.6821    |
| spearman_euclidean  | 0.7066    |
| pearson_dot         | 0.7871    |
| spearman_dot        | 0.798     |
| pearson_max         | 0.796     |
| spearman_max        | 0.819     |

#### 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      | 0.7614     |
| **spearman_cosine** | **0.7586** |
| pearson_manhattan   | 0.6203     |
| spearman_manhattan  | 0.6192     |
| pearson_euclidean   | 0.6116     |
| spearman_euclidean  | 0.6124     |
| pearson_dot         | 0.767      |
| spearman_dot        | 0.7765     |
| pearson_max         | 0.767      |
| spearman_max        | 0.7765     |

<!--
## 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

#### Unnamed Dataset


* Size: 844 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: 10.83 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.34 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>Покажите мне доступные гостиницы в Москве</code>           | <code>product query</code> | <code>1.0</code> |
  | <code>أرني العروض المتاحة على الهواتف الذكية</code>              | <code>product query</code> | <code>1.0</code> |
  | <code>Tengo problemas con el micrófono, ¿puedes ayudarme?</code> | <code>product 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: 106 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: 10.63 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.32 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                | sentence2                  | score            |
  |:---------------------------------------------------------|:---------------------------|:-----------------|
  | <code>Help me with device driver installation</code>     | <code>product query</code> | <code>0.0</code> |
  | <code>Check the status of my account verification</code> | <code>product query</code> | <code>0.0</code> |
  | <code>我怎样重置我的密码？</code>                                  | <code>product 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"
  }
  ```

### 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 |
|:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
| 0.3704 | 10   | 5.613         | 1.4994 | 0.2761                     | -                           |
| 0.7407 | 20   | 4.8872        | 1.3690 | 0.3483                     | -                           |
| 1.1111 | 30   | 3.2993        | 1.0579 | 0.4657                     | -                           |
| 1.4815 | 40   | 2.1968        | 0.6858 | 0.5935                     | -                           |
| 1.8519 | 50   | 0.7306        | 0.5191 | 0.6528                     | -                           |
| 2.2222 | 60   | 0.9746        | 0.3735 | 0.6998                     | -                           |
| 2.5926 | 70   | 0.3889        | 0.3532 | 0.7393                     | -                           |
| 2.9630 | 80   | 0.1857        | 0.3598 | 0.7554                     | -                           |
| 3.3333 | 90   | 0.2923        | 0.2795 | 0.7714                     | -                           |
| 3.7037 | 100  | 0.6776        | 0.2881 | 0.7825                     | -                           |
| 4.0741 | 110  | 0.2404        | 0.2679 | 0.7887                     | -                           |
| 4.4444 | 120  | 0.0168        | 0.2583 | 0.7918                     | -                           |
| 4.8148 | 130  | 0.0179        | 0.2273 | 0.7980                     | -                           |
| 5.1852 | 140  | 0.0006        | 0.2196 | 0.8023                     | -                           |
| 5.5556 | 150  | 0.0276        | 0.2068 | 0.8066                     | -                           |
| 5.9259 | 160  | 0.061         | 0.2063 | 0.8103                     | -                           |
| 6.2963 | 170  | 0.0265        | 0.2259 | 0.8103                     | -                           |
| 6.6667 | 180  | 0.0105        | 0.2236 | 0.8165                     | -                           |
| 7.0370 | 190  | 0.0008        | 0.2208 | 0.8177                     | -                           |
| 7.4074 | 200  | 0.361         | 0.2340 | 0.8171                     | -                           |
| 7.7778 | 210  | 0.0           | 0.2345 | 0.8190                     | -                           |
| 8.0    | 216  | -             | -      | -                          | 0.7586                      |


### 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},
}
```

<!--
## 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.*
-->