---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25300
- loss:MultipleNegativesRankingLoss

model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
    - type: pearson_manhattan
      value: .nan
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: .nan
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: .nan
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: .nan
      name: Spearman Euclidean
    - type: pearson_dot
      value: .nan
      name: Pearson Dot
    - type: spearman_dot
      value: .nan
      name: Spearman Dot
    - type: pearson_max
      value: .nan
      name: Pearson Max
    - type: spearman_max
      value: .nan
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: test eval
      type: test-eval
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
    - type: pearson_manhattan
      value: .nan
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: .nan
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: .nan
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: .nan
      name: Spearman Euclidean
    - type: pearson_dot
      value: .nan
      name: Pearson Dot
    - type: spearman_dot
      value: .nan
      name: Spearman Dot
    - type: pearson_max
      value: .nan
      name: Pearson Max
    - type: spearman_max
      value: .nan
      name: Spearman Max
---

# 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 f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 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': 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
```

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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric             | Value   |
|:-------------------|:--------|
| pearson_cosine     | nan     |
| spearman_cosine    | nan     |
| pearson_manhattan  | nan     |
| spearman_manhattan | nan     |
| pearson_euclidean  | nan     |
| spearman_euclidean | nan     |
| pearson_dot        | nan     |
| spearman_dot       | nan     |
| pearson_max        | nan     |
| **spearman_max**   | **nan** |

#### Semantic Similarity
* Dataset: `test-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value   |
|:-------------------|:--------|
| pearson_cosine     | nan     |
| spearman_cosine    | nan     |
| pearson_manhattan  | nan     |
| spearman_manhattan | nan     |
| pearson_euclidean  | nan     |
| spearman_euclidean | nan     |
| pearson_dot        | nan     |
| spearman_dot       | nan     |
| pearson_max        | nan     |
| **spearman_max**   | **nan** |

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## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 25,300 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>texts</code>
* 
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 5,422 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>texts</code>

* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | spearman_max | test-eval_spearman_max |
|:------:|:----:|:-------------:|:---------------:|:------------:|:----------------------:|
| 0.0632 | 100  | 4.9949        | 2.0402          | nan          | -                      |
| 0.1264 | 200  | 1.9907        | 1.8355          | nan          | -                      |
| 0.1896 | 300  | 1.8898        | 1.9113          | nan          | -                      |
| 0.2528 | 400  | 1.8334        | 1.7294          | nan          | -                      |
| 0.3161 | 500  | 1.7497        | 1.7388          | nan          | -                      |
| 0.3793 | 600  | 1.6786        | 1.6524          | nan          | -                      |
| 0.4425 | 700  | 1.6914        | 1.6440          | nan          | -                      |
| 0.5057 | 800  | 1.6303        | 1.6218          | nan          | -                      |
| 0.5689 | 900  | 1.6388        | 1.6212          | nan          | -                      |
| 0.6321 | 1000 | 1.6032        | 1.6182          | nan          | -                      |
| 0.6953 | 1100 | 1.5957        | 1.5945          | nan          | -                      |
| 0.7585 | 1200 | 1.6303        | 1.5753          | nan          | -                      |
| 0.8217 | 1300 | 1.5978        | 1.5705          | nan          | -                      |
| 0.8850 | 1400 | 1.554         | 1.5663          | nan          | -                      |
| 0.9482 | 1500 | 1.4899        | 1.5525          | nan          | -                      |
| 1.0114 | 1600 | 1.4792        | 1.5962          | nan          | -                      |
| 1.0746 | 1700 | 1.4683        | 1.5481          | nan          | -                      |
| 1.1378 | 1800 | 1.4615        | 1.5256          | nan          | -                      |
| 1.2010 | 1900 | 1.4395        | 1.5321          | nan          | -                      |
| 1.2642 | 2000 | 1.3524        | 1.5148          | nan          | -                      |
| 1.3274 | 2100 | 1.3876        | 1.5356          | nan          | -                      |
| 1.3906 | 2200 | 1.4376        | 1.4979          | nan          | -                      |
| 1.4539 | 2300 | 1.4187        | 1.5046          | nan          | -                      |
| 1.5171 | 2400 | 1.4604        | 1.5011          | nan          | -                      |
| 1.5803 | 2500 | 1.4194        | 1.4851          | nan          | -                      |
| 1.6435 | 2600 | 1.4057        | 1.4897          | nan          | -                      |
| 1.7067 | 2700 | 1.3683        | 1.4921          | nan          | -                      |
| 1.7699 | 2800 | 1.3333        | 1.4797          | nan          | -                      |
| 1.8331 | 2900 | 1.3961        | 1.4752          | nan          | -                      |
| 1.8963 | 3000 | 1.3718        | 1.4693          | nan          | -                      |
| 1.9595 | 3100 | 1.3263        | 1.4699          | nan          | -                      |
| 2.0    | 3164 | -             | -               | -            | nan                    |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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