sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:25310 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25310
- loss:CosineSimilarityLoss
widget:
- source_sentence: encryption algorithms for mobile transactions
  sentences:
  - equipaggiamento per sport acquatici
  - finanziamenti a lungo termine per privati
  - encryption algorithms for mobile banking
- source_sentence: tecnologie di liofilizzazione per frutta e verdura
  sentences:
  - serbatoi di fermentazione in acciaio inox per cantine
  - impianti di liofilizzazione per frutta e verdura
  - medical cannulas
- source_sentence: servizi di installazione di cavi sottomarini
  sentences:
  - servizi di installazione di cavi sottomarini
  - custom spinal fusion implants
  - soluzioni disinfettanti per il settore sanitario
- source_sentence: antifouling paint for yachts
  sentences:
  - sistemi di ventilazione con controllo umidità integrato
  - robot per la movimentazione interna
  - vernici per automobili
- source_sentence: materiali isolanti per sistemi radianti a soffitto
  sentences:
  - Produzione di contenuti per social media nel settore moda.
  - privacy and data protection training
  - materiali isolanti per edifici
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: custom dataset
      type: custom_dataset
    metrics:
    - type: pearson_cosine
      value: 0.7375739325911802
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7391641074346988
      name: Spearman Cosine
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dataset
      type: all_nli_dataset
    metrics:
    - type: cosine_accuracy
      value: 0.9319148659706116
      name: Cosine Accuracy
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsbenchmark
      type: stsbenchmark
    metrics:
    - type: pearson_cosine
      value: 0.8403996196393042
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.83421898505618
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
  (2): Normalize()
)
```

## 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("LucaZilli/all_mpnet_base_v2_190225")
# Run inference
sentences = [
    'materiali isolanti per sistemi radianti a soffitto',
    'materiali isolanti per edifici',
    'privacy and data protection training',
]
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

#### Semantic Similarity

* Datasets: `custom_dataset` and `stsbenchmark`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | custom_dataset | stsbenchmark |
|:--------------------|:---------------|:-------------|
| pearson_cosine      | 0.7376         | 0.8404       |
| **spearman_cosine** | **0.7392**     | **0.8342**   |

#### Triplet

* Dataset: `all_nli_dataset`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9319** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 25,310 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: 4 tokens</li><li>mean: 13.32 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.06 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                        | sentence2                                                           | score            |
  |:---------------------------------------------------------------------------------|:--------------------------------------------------------------------|:-----------------|
  | <code>ottimizzazione dei tempi di produzione per capi sartoriali di lusso</code> | <code>strumenti per l'ottimizzazione dei tempi di produzione</code> | <code>0.6</code> |
  | <code>software di programmazione robotica per lucidatura</code>                  | <code>software gestionale generico</code>                           | <code>0.4</code> |
  | <code>rete di sensori per l'analisi del suolo in tempo reale</code>              | <code>software per gestione aziendale</code>                        | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 3,164 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: 5 tokens</li><li>mean: 13.61 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.39 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                             | sentence2                                                                     | score            |
  |:------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------|
  | <code>ispezioni regolari per camion aziendali</code>  | <code>ispezioni regolari per camion di consegna</code>                        | <code>1.0</code> |
  | <code>blister packaging machines GMP compliant</code> | <code>food packaging machines</code>                                          | <code>0.4</code> |
  | <code>EMI shielding paints for electronics</code>     | <code>Vernici per schermatura elettromagnetica dispositivi elettronici</code> | <code>0.8</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5e-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`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | custom_dataset_spearman_cosine | all_nli_dataset_cosine_accuracy | stsbenchmark_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:------------------------------:|:-------------------------------:|:----------------------------:|
| -1     | -1   | -             | -               | 0.7392                         | 0.9319                          | 0.8342                       |
| 0.0632 | 100  | 0.0484        | -               | -                              | -                               | -                            |
| 0.1264 | 200  | 0.0389        | 0.0363          | -                              | -                               | -                            |
| 0.1896 | 300  | 0.0377        | -               | -                              | -                               | -                            |
| 0.2528 | 400  | 0.033         | 0.0307          | -                              | -                               | -                            |
| 0.3161 | 500  | 0.0302        | -               | -                              | -                               | -                            |
| 0.3793 | 600  | 0.0329        | 0.0298          | -                              | -                               | -                            |
| 0.4425 | 700  | 0.0315        | -               | -                              | -                               | -                            |
| 0.5057 | 800  | 0.0308        | 0.0275          | -                              | -                               | -                            |
| 0.5689 | 900  | 0.0309        | -               | -                              | -                               | -                            |
| 0.6321 | 1000 | 0.0279        | 0.0282          | -                              | -                               | -                            |
| 0.6953 | 1100 | 0.0299        | -               | -                              | -                               | -                            |
| 0.7585 | 1200 | 0.0286        | 0.0271          | -                              | -                               | -                            |
| 0.8217 | 1300 | 0.0292        | -               | -                              | -                               | -                            |
| 0.8850 | 1400 | 0.027         | 0.0261          | -                              | -                               | -                            |
| 0.9482 | 1500 | 0.0254        | -               | -                              | -                               | -                            |
| 1.0114 | 1600 | 0.0237        | 0.0242          | -                              | -                               | -                            |
| 1.0746 | 1700 | 0.0196        | -               | -                              | -                               | -                            |
| 1.1378 | 1800 | 0.0196        | 0.0242          | -                              | -                               | -                            |
| 1.2010 | 1900 | 0.021         | -               | -                              | -                               | -                            |
| 1.2642 | 2000 | 0.0225        | 0.0267          | -                              | -                               | -                            |
| 1.3274 | 2100 | 0.022         | -               | -                              | -                               | -                            |
| 1.3906 | 2200 | 0.0203        | 0.0246          | -                              | -                               | -                            |
| 1.4539 | 2300 | 0.0178        | -               | -                              | -                               | -                            |
| 1.5171 | 2400 | 0.019         | 0.0240          | -                              | -                               | -                            |
| 1.5803 | 2500 | 0.0209        | -               | -                              | -                               | -                            |
| 1.6435 | 2600 | 0.0186        | 0.0240          | -                              | -                               | -                            |
| 1.7067 | 2700 | 0.0261        | -               | -                              | -                               | -                            |
| 1.7699 | 2800 | 0.0193        | 0.0246          | -                              | -                               | -                            |
| 1.8331 | 2900 | 0.02          | -               | -                              | -                               | -                            |
| 1.8963 | 3000 | 0.0196        | 0.0240          | -                              | -                               | -                            |
| 1.9595 | 3100 | 0.0186        | -               | -                              | -                               | -                            |
| 2.0228 | 3200 | 0.0164        | 0.0226          | -                              | -                               | -                            |
| 2.0860 | 3300 | 0.0122        | -               | -                              | -                               | -                            |
| 2.1492 | 3400 | 0.0123        | 0.0221          | -                              | -                               | -                            |
| 2.2124 | 3500 | 0.0135        | -               | -                              | -                               | -                            |
| 2.2756 | 3600 | 0.0134        | 0.0226          | -                              | -                               | -                            |
| 2.3388 | 3700 | 0.0128        | -               | -                              | -                               | -                            |
| 2.4020 | 3800 | 0.0126        | 0.0231          | -                              | -                               | -                            |
| 2.4652 | 3900 | 0.0134        | -               | -                              | -                               | -                            |
| 2.5284 | 4000 | 0.0142        | 0.0231          | -                              | -                               | -                            |
| 2.5917 | 4100 | 0.0124        | -               | -                              | -                               | -                            |
| 2.6549 | 4200 | 0.0132        | 0.0215          | -                              | -                               | -                            |
| 2.7181 | 4300 | 0.0136        | -               | -                              | -                               | -                            |
| 2.7813 | 4400 | 0.013         | 0.0218          | -                              | -                               | -                            |
| 2.8445 | 4500 | 0.0127        | -               | -                              | -                               | -                            |
| 2.9077 | 4600 | 0.0126        | 0.0213          | -                              | -                               | -                            |
| 2.9709 | 4700 | 0.0133        | -               | -                              | -                               | -                            |
| 3.0341 | 4800 | 0.0103        | 0.0209          | -                              | -                               | -                            |
| 3.0973 | 4900 | 0.0086        | -               | -                              | -                               | -                            |
| 3.1606 | 5000 | 0.0088        | 0.0211          | -                              | -                               | -                            |
| 3.2238 | 5100 | 0.0081        | -               | -                              | -                               | -                            |
| 3.2870 | 5200 | 0.0079        | 0.0212          | -                              | -                               | -                            |
| 3.3502 | 5300 | 0.0094        | -               | -                              | -                               | -                            |
| 3.4134 | 5400 | 0.0086        | 0.0210          | -                              | -                               | -                            |
| 3.4766 | 5500 | 0.0089        | -               | -                              | -                               | -                            |
| 3.5398 | 5600 | 0.009         | 0.0209          | -                              | -                               | -                            |
| 3.6030 | 5700 | 0.0086        | -               | -                              | -                               | -                            |
| 3.6662 | 5800 | 0.0087        | 0.0213          | -                              | -                               | -                            |
| 3.7295 | 5900 | 0.0085        | -               | -                              | -                               | -                            |
| 3.7927 | 6000 | 0.0094        | 0.0213          | -                              | -                               | -                            |
| 3.8559 | 6100 | 0.0095        | -               | -                              | -                               | -                            |
| 3.9191 | 6200 | 0.01          | 0.0212          | -                              | -                               | -                            |
| 3.9823 | 6300 | 0.0097        | -               | -                              | -                               | -                            |
| 4.0455 | 6400 | 0.0081        | 0.0214          | -                              | -                               | -                            |
| 4.1087 | 6500 | 0.0095        | -               | -                              | -                               | -                            |
| 4.1719 | 6600 | 0.0083        | 0.0208          | -                              | -                               | -                            |
| 4.2351 | 6700 | 0.0082        | -               | -                              | -                               | -                            |
| 4.2984 | 6800 | 0.0074        | 0.0208          | -                              | -                               | -                            |
| 4.3616 | 6900 | 0.0076        | -               | -                              | -                               | -                            |
| 4.4248 | 7000 | 0.0072        | 0.0205          | -                              | -                               | -                            |
| 4.4880 | 7100 | 0.0072        | -               | -                              | -                               | -                            |
| 4.5512 | 7200 | 0.007         | 0.0205          | -                              | -                               | -                            |
| 4.6144 | 7300 | 0.0069        | -               | -                              | -                               | -                            |
| 4.6776 | 7400 | 0.0069        | 0.0203          | -                              | -                               | -                            |
| 4.7408 | 7500 | 0.0068        | -               | -                              | -                               | -                            |
| 4.8040 | 7600 | 0.0073        | 0.0204          | -                              | -                               | -                            |
| 4.8673 | 7700 | 0.0063        | -               | -                              | -                               | -                            |
| 4.9305 | 7800 | 0.0065        | 0.0203          | -                              | -                               | -                            |
| 4.9937 | 7900 | 0.0069        | -               | -                              | -                               | -                            |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.1
- 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",
}
```

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