README.md

---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: an elephant with a leaf on its back
  sentences:
  - an elephant is walking through the woods
  - a white truck with a white sign on it
  - a bathroom with a tub and sink
- source_sentence: a man and woman hugging
  sentences:
  - a couple hugging in the street
  - a 3d model of a robot in purple and silver
  - a woman jumping in the air on a field
- source_sentence: a silhouette of a man holding a sword in the sky
  sentences:
  - strawberry ice cream on a plate with strawberries
  - a banana sitting on a chair
  - a silhouette of a man holding a sword in the sky
- source_sentence: a girl in a chinese costume holding a spear
  sentences:
  - a young girl in a traditional asian dress holding a stick
  - a man is chopping a piece of wood on a cutting board
  - a surfer riding a large wave on a surfboard
- source_sentence: a bathroom with a bathtub and toilet
  sentences:
  - a bathroom with a white tub and sink
  - a kitchen with stainless steel appliances and wood cabinets
  - a woman in pink lingerie with a flower crown
---

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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) <!-- at revision bef3689366be4ad4b62c8e1cec013639bea3c86a -->
- **Maximum Sequence Length:** 512 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': 512, '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})
)
```

## 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("kasraarabi/finetuned-caption-embedding")
# Run inference
sentences = [
    'a bathroom with a bathtub and toilet',
    'a bathroom with a white tub and sink',
    'a woman in pink lingerie with a flower crown',
]
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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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 10,000 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: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.65 tokens</li><li>max: 17 tokens</li></ul> |
* Samples:
  | sentence_0                                          | sentence_1                                                             |
  |:----------------------------------------------------|:-----------------------------------------------------------------------|
  | <code>two women cutting a cake</code>               | <code>two women cutting a cake</code>                                  |
  | <code>a man with long white hair and a beard</code> | <code>a man with a long white beard</code>                             |
  | <code>a bench is sitting on the sidewalk</code>     | <code>a bench is sitting on the sidewalk in front of a building</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

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 140
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 140
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch    | Step  | Training Loss |
|:--------:|:-----:|:-------------:|
| 3.1847   | 500   | 0.1576        |
| 6.3694   | 1000  | 0.1099        |
| 9.5541   | 1500  | 0.0799        |
| 12.7389  | 2000  | 0.0627        |
| 15.9236  | 2500  | 0.0569        |
| 19.1083  | 3000  | 0.0503        |
| 22.2930  | 3500  | 0.043         |
| 25.4777  | 4000  | 0.041         |
| 28.6624  | 4500  | 0.0357        |
| 31.8471  | 5000  | 0.0338        |
| 35.0318  | 5500  | 0.0326        |
| 38.2166  | 6000  | 0.0299        |
| 41.4013  | 6500  | 0.0319        |
| 44.5860  | 7000  | 0.0286        |
| 47.7707  | 7500  | 0.0266        |
| 50.9554  | 8000  | 0.0269        |
| 54.1401  | 8500  | 0.0253        |
| 57.3248  | 9000  | 0.0264        |
| 60.5096  | 9500  | 0.0247        |
| 63.6943  | 10000 | 0.0235        |
| 66.8790  | 10500 | 0.0241        |
| 70.0637  | 11000 | 0.0224        |
| 73.2484  | 11500 | 0.0208        |
| 76.4331  | 12000 | 0.0215        |
| 79.6178  | 12500 | 0.0224        |
| 82.8025  | 13000 | 0.0204        |
| 85.9873  | 13500 | 0.0185        |
| 89.1720  | 14000 | 0.02          |
| 92.3567  | 14500 | 0.0189        |
| 95.5414  | 15000 | 0.0191        |
| 98.7261  | 15500 | 0.0186        |
| 101.9108 | 16000 | 0.0183        |
| 105.0955 | 16500 | 0.019         |
| 108.2803 | 17000 | 0.0162        |
| 111.4650 | 17500 | 0.0181        |
| 114.6497 | 18000 | 0.0173        |
| 117.8344 | 18500 | 0.0187        |
| 121.0191 | 19000 | 0.0159        |
| 124.2038 | 19500 | 0.0172        |
| 127.3885 | 20000 | 0.0164        |
| 130.5732 | 20500 | 0.0168        |
| 133.7580 | 21000 | 0.0157        |
| 136.9427 | 21500 | 0.0156        |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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",
}
```

#### 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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