---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7851
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: did I gain any profits over the past 10 days
  sentences:
  - Which stocks have a strong potential to see a 10% increase in the next 10 months?
  - Did I make any money from trading in the last 10 days
  - Which stocks have a strong potential to go up by 10% in the next 10 months?
- source_sentence: Can you show me my holdings?
  sentences:
  - Reveal my highest-risk assets
  - Display my riskiest investment holdings
  - 'I''d like to see my portfolio details '
- source_sentence: Do I have any stocks in my portfolio?
  sentences:
  - Are there any shares of stock included in my portfolio?
  - Unfold my individualized fintech recommendations
  - What's the numerical assessment of my portfolio?
- source_sentence: View my report card
  sentences:
  - Which sectors are the most attractive to investors in my portfolio
  - Recalibrate portfolio from stocks to mutual fund holdings
  - Get my account overview
- source_sentence: Which of my investments have the highest volatility?
  sentences:
  - Can I see a yearly analysis of my returns
  - Have I committed resources to any equity-driven investment funds?
  - Which of my assets show the most pronounced fluctuations in market value?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on microsoft/mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 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': 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("pawan2411/semantic-embedding_2")
# Run inference
sentences = [
    'Which of my investments have the highest volatility?',
    'Which of my assets show the most pronounced fluctuations in market value?',
    'Can I see a yearly analysis of my returns',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 7,851 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: 9.57 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.07 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
  | sentence_0                                                            | sentence_1                                                                                       |
  |:----------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | <code>Show me how to switch my stock portfolio to mutual funds</code> | <code>What steps should I take to replace my stock holdings with mutual fund investments?</code> |
  | <code>View my holdings</code>                                         | <code>See my investment portfolio</code>                                                         |
  | <code>How did my portfolio perform last week ?</code>                 | <code>Can you give me a rundown of my portfolio's performance for the past week?</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`: 50
- `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`: 50
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch    | Step  | Training Loss |
|:--------:|:-----:|:-------------:|
| 4.0650   | 500   | 2.1067        |
| 8.1301   | 1000  | 0.8233        |
| 12.1951  | 1500  | 0.6455        |
| 16.2602  | 2000  | 0.5768        |
| 20.3252  | 2500  | 0.5378        |
| 24.3902  | 3000  | 0.5155        |
| 28.4553  | 3500  | 0.499         |
| 32.5203  | 4000  | 0.4906        |
| 36.5854  | 4500  | 0.4841        |
| 40.6504  | 5000  | 0.4801        |
| 44.7154  | 5500  | 0.4746        |
| 48.7805  | 6000  | 0.4718        |
| 52.8455  | 6500  | 0.47          |
| 56.9106  | 7000  | 0.468         |
| 60.9756  | 7500  | 0.4655        |
| 65.0407  | 8000  | 0.4634        |
| 69.1057  | 8500  | 0.462         |
| 73.1707  | 9000  | 0.4604        |
| 77.2358  | 9500  | 0.46          |
| 81.3008  | 10000 | 0.4598        |
| 85.3659  | 10500 | 0.458         |
| 89.4309  | 11000 | 0.4574        |
| 93.4959  | 11500 | 0.4566        |
| 97.5610  | 12000 | 0.4565        |
| 101.6260 | 12500 | 0.4558        |
| 105.6911 | 13000 | 0.455         |
| 109.7561 | 13500 | 0.4551        |
| 113.8211 | 14000 | 0.455         |
| 117.8862 | 14500 | 0.4544        |
| 121.9512 | 15000 | 0.4533        |
| 126.0163 | 15500 | 0.4543        |
| 130.0813 | 16000 | 0.4535        |
| 134.1463 | 16500 | 0.4532        |
| 138.2114 | 17000 | 0.4522        |
| 142.2764 | 17500 | 0.4536        |
| 146.3415 | 18000 | 0.4521        |
| 4.0650   | 500   | 0.4898        |
| 8.1301   | 1000  | 0.4737        |
| 12.1951  | 1500  | 0.4681        |
| 16.2602  | 2000  | 0.4669        |
| 20.3252  | 2500  | 0.4645        |
| 24.3902  | 3000  | 0.4626        |
| 28.4553  | 3500  | 0.4586        |
| 32.5203  | 4000  | 0.4568        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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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