README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:48393
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Tennis champ Rafael Nadal lunges to return a ball.
  sentences:
  - The tennis champ has decided to quit playing tennis.
  - A woman stands alone at a restaurant.
  - A blond woman running
- source_sentence: Small girl getting her face painted.
  sentences:
  - A Meijer in Illinois selling groceries.
  - Two men are posing together.
  - A small girl washing her face.
- source_sentence: because too too often they're can be extremism that that hurts
    from from any direction regardless of whatever whatever you're arguing or concerned
    about and
  sentences:
  - If you could stir the mothers, you are done.
  - Extremism is bad.
  - Steve Ballmer is a college friend of mine.
- source_sentence: The dog jumps over the log with a stick in its mouth.
  sentences:
  - A girl in red jumps outdoors.
  - The dog is running around with something in it's mouth.
  - The price is lower than what they pay.
- source_sentence: A man in black shirt sits on a stool while trying to sell stuffed
    animals.
  sentences:
  - A man is sitting on a stool.
  - A pooch runs through the grass.
  - A young lady is sitting on a bench at the bus stop.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: eval
      type: eval
    metrics:
    - type: cosine_accuracy@1
      value: 0.0004959394953815635
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.36964023722439193
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4739321802740066
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5881015849399707
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0004959394953815635
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.12321341240813066
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09478643605480129
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05881015849399707
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0004959394953815635
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.36964023722439193
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4739321802740066
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5881015849399707
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3037659752455345
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2120033429995685
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22559046634335145
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.0005579319323042589
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.3696609013700329
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.4739321802740066
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.5881429132312525
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.0005579319323042589
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.12322030045667762
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.09478643605480132
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.05881429132312524
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.0005579319323042589
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.3696609013700329
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.4739321802740066
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.5881429132312525
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.30380430047413587
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.2120435150827015
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.22562658480145822
      name: Dot Map@100
---

# 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
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("richie-ghost/sentence-transformers-all-mpnet-base-v2")
# Run inference
sentences = [
    'A man in black shirt sits on a stool while trying to sell stuffed animals.',
    'A man is sitting on a stool.',
    'A young lady is sitting on a bench at the bus stop.',
]
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

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0005     |
| cosine_accuracy@3   | 0.3696     |
| cosine_accuracy@5   | 0.4739     |
| cosine_accuracy@10  | 0.5881     |
| cosine_precision@1  | 0.0005     |
| cosine_precision@3  | 0.1232     |
| cosine_precision@5  | 0.0948     |
| cosine_precision@10 | 0.0588     |
| cosine_recall@1     | 0.0005     |
| cosine_recall@3     | 0.3696     |
| cosine_recall@5     | 0.4739     |
| cosine_recall@10    | 0.5881     |
| cosine_ndcg@10      | 0.3038     |
| cosine_mrr@10       | 0.212      |
| cosine_map@100      | 0.2256     |
| dot_accuracy@1      | 0.0006     |
| dot_accuracy@3      | 0.3697     |
| dot_accuracy@5      | 0.4739     |
| dot_accuracy@10     | 0.5881     |
| dot_precision@1     | 0.0006     |
| dot_precision@3     | 0.1232     |
| dot_precision@5     | 0.0948     |
| dot_precision@10    | 0.0588     |
| dot_recall@1        | 0.0006     |
| dot_recall@3        | 0.3697     |
| dot_recall@5        | 0.4739     |
| dot_recall@10       | 0.5881     |
| dot_ndcg@10         | 0.3038     |
| dot_mrr@10          | 0.212      |
| **dot_map@100**     | **0.2256** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 48,393 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 18.73 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.35 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
  | sentence_0                                                           | sentence_1                                                        |
  |:---------------------------------------------------------------------|:------------------------------------------------------------------|
  | <code>A group of kids in red and white playing soccer.</code>        | <code>There are kids playing ball in a soccer tournament.</code>  |
  | <code>I had a great time at the theme park with my family.</code>    | <code>Did you have fun at the theme park with your family?</code> |
  | <code>A black and white elderly gentlemen riding an am-track.</code> | <code>A man is on a train.</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`: 4
- `multi_dataset_batch_sampler`: round_robin

#### 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
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: round_robin

</details>

### Training Logs
| Epoch  | Step  | Training Loss | eval_dot_map@100 |
|:------:|:-----:|:-------------:|:----------------:|
| 0.1653 | 500   | 0.0446        | 0.2186           |
| 0.3306 | 1000  | 0.0544        | 0.2226           |
| 0.4959 | 1500  | 0.0419        | 0.2191           |
| 0.6612 | 2000  | 0.0532        | 0.2210           |
| 0.8264 | 2500  | 0.0438        | 0.2209           |
| 0.9917 | 3000  | 0.0422        | 0.2220           |
| 1.0    | 3025  | -             | 0.2225           |
| 1.1570 | 3500  | 0.021         | 0.2236           |
| 1.3223 | 4000  | 0.0163        | 0.2243           |
| 1.4876 | 4500  | 0.0158        | 0.2221           |
| 1.6529 | 5000  | 0.0178        | 0.2221           |
| 1.8182 | 5500  | 0.0154        | 0.2222           |
| 1.9835 | 6000  | 0.0145        | 0.2228           |
| 2.0    | 6050  | -             | 0.2247           |
| 2.1488 | 6500  | 0.0098        | 0.2250           |
| 2.3140 | 7000  | 0.0076        | 0.2239           |
| 2.4793 | 7500  | 0.0069        | 0.2253           |
| 2.6446 | 8000  | 0.0073        | 0.2245           |
| 2.8099 | 8500  | 0.0063        | 0.2245           |
| 2.9752 | 9000  | 0.0074        | 0.2251           |
| 3.0    | 9075  | -             | 0.2251           |
| 3.1405 | 9500  | 0.0044        | 0.2256           |
| 3.3058 | 10000 | 0.0043        | 0.2259           |
| 3.4711 | 10500 | 0.0038        | 0.2261           |
| 3.6364 | 11000 | 0.0039        | 0.2256           |
| 3.8017 | 11500 | 0.0037        | 0.2251           |
| 3.9669 | 12000 | 0.0043        | 0.2256           |
| 4.0    | 12100 | -             | 0.2256           |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.1
- Datasets: 3.0.2
- 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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