---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3772
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
base_model: jinaai/jina-embedding-b-en-v1
widget:
- source_sentence: do I possess any funds that are not performing
  sentences:
  - How did my portfolio perform this month?
  - Show me my best performing holdings
  - do I hold any funds that haven't been performing well
- source_sentence: did I earn any money over the past 18 days
  sentences:
  - What are the returns of my portfolio in the past 6 months?
  - did I gain any profits over the past 18 days
  - How are my investments performing ?
- source_sentence: What stocks should I expect to rise 100% in the next month?
  sentences:
  - performance of my portfolio over the last 3 years
  - Tell me which of my stocks will go up 100% in next month
  - How much X do I have in my portfolio
- source_sentence: Mere portfolio kaise chal raha hai?
  sentences:
  - What are my top performing funds?
  - Mere funds kese chal rahe hai?
  - How can I improve my returns ?
- source_sentence: how many folios do I own
  sentences:
  - Which of my investments have the highest volatility?
  - show my portfolio scheme wise
  - Show all my folios
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 jinaai/jina-embedding-b-en-v1
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: test eval
      type: test-eval
    metrics:
    - type: cosine_accuracy@1
      value: 0.8756613756613757
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9894179894179894
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9973544973544973
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8756613756613757
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3298059964726632
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19947089947089944
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8756613756613757
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9894179894179894
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9973544973544973
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9482145369797552
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9304232804232803
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9304232804232804
      name: Cosine Map@100
---

# SentenceTransformer based on jinaai/jina-embedding-b-en-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1). 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:** [jinaai/jina-embedding-b-en-v1](https://huggingface.co/jinaai/jina-embedding-b-en-v1) <!-- at revision 32aa658e5ceb90793454d22a57d8e3a14e699516 -->
- **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: T5EncoderModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'how many folios do I own',
    'Show all my folios',
    'Which of my investments have the highest volatility?',
]
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   | 0.8757     |
| cosine_accuracy@3   | 0.9894     |
| cosine_accuracy@5   | 0.9974     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8757     |
| cosine_precision@3  | 0.3298     |
| cosine_precision@5  | 0.1995     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8757     |
| cosine_recall@3     | 0.9894     |
| cosine_recall@5     | 0.9974     |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9482** |
| cosine_mrr@10       | 0.9304     |
| cosine_map@100      | 0.9304     |

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## Training Details

### Training Datasets

#### Unnamed Dataset

* Size: 1,886 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: 4 tokens</li><li>mean: 11.34 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.0 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>how can I reduce the risk in my portfolio?</code>             | <code>are there any ways to lower risk in my portfolio ?</code> | <code>1.0</code> |
  | <code>What stocks do I own that have the highest volatility?</code> | <code>Which of my stocks are most volatile?</code>              | <code>1.0</code> |
  | <code>What are the trends in investments for people like me?</code> | <code>What are others like me investing in?</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: 1,886 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: 4 tokens</li><li>mean: 11.11 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.82 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>What parts of my portfolio are successful?</code> | <code>What's going well in my portfolio</code>     | <code>1.0</code> |
  | <code>Reveal my investment holdings</code>              | <code>Show my holdings</code>                      | <code>1.0</code> |
  | <code>What do others in my situation invest in?</code>  | <code>What are others like me investing in?</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    | 118  | -             | 0.9051                   |
| 2.0    | 236  | -             | 0.9165                   |
| 3.0    | 354  | -             | 0.9293                   |
| 4.0    | 472  | -             | 0.9329                   |
| 4.2373 | 500  | 0.206         | 0.9338                   |
| 5.0    | 590  | -             | 0.9338                   |
| 6.0    | 708  | -             | 0.9363                   |
| 7.0    | 826  | -             | 0.9403                   |
| 8.0    | 944  | -             | 0.9466                   |
| 8.4746 | 1000 | 0.1406        | 0.9482                   |


### 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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