---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3320
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: How did my portfolio perform during the last 18 days?
  sentences:
  - What is the performance of my portfolio over the last 18 days?
  - 'Show me the geographic distribution of my investments

    '
  - Show me recommendations on improving returns and risk
- source_sentence: I'd like to know my sector distribution.
  sentences:
  - Show my market cap breakdown
  - In which funds am I paying highest fees
  - What is my sector allocation?
- source_sentence: Do I have any equity funds in my portfolio?
  sentences:
  - Show me my recommendations
  - Do I hold any equity funds?
  - Show me some swap recommendations on my portfolio
- source_sentence: Is my portfolio ready for changes in the market?
  sentences:
  - Is my current portfolio balanced properly for market changes?
  - Have I got Swap recommendations on risk
  - is there any room for improvement in my portfolio
- source_sentence: Which stocks would be best to trade for funds?
  sentences:
  - Is my portfolio beating the market?
  - Show me ways to reduce my cost
  - Which of my stocks should I swap for funds?
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: test eval
      type: test-eval
    metrics:
    - type: cosine_accuracy@1
      value: 0.8975903614457831
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9969879518072289
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8975903614457831
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33232931726907633
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8975903614457831
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9969879518072289
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9600230102760412
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9460341365461847
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9460341365461847
      name: Cosine 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 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
- **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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Which stocks would be best to trade for funds?',
    'Which of my stocks should I swap for funds?',
    'Is my portfolio beating the market?',
]
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: `test-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   | 0.8976   |
| cosine_accuracy@3   | 0.997    |
| cosine_accuracy@5   | 1.0      |
| cosine_accuracy@10  | 1.0      |
| cosine_precision@1  | 0.8976   |
| cosine_precision@3  | 0.3323   |
| cosine_precision@5  | 0.2      |
| cosine_precision@10 | 0.1      |
| cosine_recall@1     | 0.8976   |
| cosine_recall@3     | 0.997    |
| cosine_recall@5     | 1.0      |
| cosine_recall@10    | 1.0      |
| **cosine_ndcg@10**  | **0.96** |
| cosine_mrr@10       | 0.946    |
| cosine_map@100      | 0.946    |

<!--
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Datasets

#### Unnamed Dataset

* Size: 1,660 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 12.06 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.67 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                      | sentence_1                                       | label            |
  |:----------------------------------------------------------------|:-------------------------------------------------|:-----------------|
  | <code>Please suggest some ideas for me.</code>                  | <code>Suggest recommendations for me</code>      | <code>1.0</code> |
  | <code>Mere paas jo stocks hain unhe dikhaiye</code>             | <code>Mujhe apne stocks dikhao</code>            | <code>1.0</code> |
  | <code>Is my portfolio performing better than the market?</code> | <code>Is my portfolio beating the market?</code> | <code>1.0</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"
  }
  ```

#### Unnamed Dataset

* Size: 1,660 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 12.12 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                           | sentence_1                                             | label            |
  |:---------------------------------------------------------------------|:-------------------------------------------------------|:-----------------|
  | <code>I'd like to see my sector allocation, please.</code>           | <code>Can you show my sector allocation?</code>        | <code>1.0</code> |
  | <code>Which of my funds are failing to perform?</code>               | <code>Which of my funds aren't doing well?</code>      | <code>1.0</code> |
  | <code>Can you list my investments based on their ESG ratings?</code> | <code>Show my investments sorted by ESG rating.</code> | <code>1.0</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"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 10
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 10
- `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 | test-eval_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:------------------------:|
| 1.0    | 104  | -             | 0.9079                   |
| 2.0    | 208  | -             | 0.9334                   |
| 3.0    | 312  | -             | 0.9448                   |
| 4.0    | 416  | -             | 0.9447                   |
| 4.8077 | 500  | 0.1486        | 0.9529                   |
| 5.0    | 520  | -             | 0.9543                   |
| 6.0    | 624  | -             | 0.9540                   |
| 7.0    | 728  | -             | 0.9560                   |
| 8.0    | 832  | -             | 0.9561                   |
| 9.0    | 936  | -             | 0.9560                   |
| 9.6154 | 1000 | 0.1024        | 0.9600                   |


### Framework Versions
- Python: 3.12.5
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0
- Accelerate: 1.5.2
- Datasets: 3.4.1
- Tokenizers: 0.21.1

## 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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