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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3688
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
base_model: jinaai/jina-embedding-b-en-v1
widget:
- source_sentence: Am I invested in emerging markets?
sentences:
- Do I have any investments in emerging markets?
- Do I have financials in my portfolio?
- Show me my recommendations
- source_sentence: how has my portfolio performed in the past 3 years
sentences:
- 'Show me the geographic distribution of my investments
'
- How can I improve my returns?
- performance of my portfolio over the last 3 years
- source_sentence: What percent of my holdings are in X?
sentences:
- What percentage of my portfolio is in X
- Show my worst performing funds?
- Are my ETFs giving better returns compare to my mutual funds?
- source_sentence: How's my investments in stocks doing?
sentences:
- How is my stock portfolio performing?
- Show my worst stocks?
- Are there any hidden fees in my portfolio that I could reduce?
- source_sentence: Can you show me my portfolio's returns for all the years?
sentences:
- Need to change my risk appetite
- My portfolio returns over all the years
- How is my portfolio doing?
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: 0.8699186991869918
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.989159891598916
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.994579945799458
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8699186991869918
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32971996386630537
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19891598915989162
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8699186991869918
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.989159891598916
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.994579945799458
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9468139420641406
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9285004516711834
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9285004516711834
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 = [
"Can you show me my portfolio's returns for all the years?",
'My portfolio returns over all the years',
'Need to change my risk appetite',
]
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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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 | 0.8699 |
| cosine_accuracy@3 | 0.9892 |
| cosine_accuracy@5 | 0.9946 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8699 |
| cosine_precision@3 | 0.3297 |
| cosine_precision@5 | 0.1989 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8699 |
| cosine_recall@3 | 0.9892 |
| cosine_recall@5 | 0.9946 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9468** |
| cosine_mrr@10 | 0.9285 |
| cosine_map@100 | 0.9285 |
<!--
## Bias, Risks and Limitations
*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.*
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## Training Details
### Training Datasets
#### Unnamed Dataset
* Size: 1,844 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: 11.37 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>what can I do to improve my portfolio?</code> | <code>is there any room for improvement in my portfolio</code> | <code>1.0</code> |
| <code>I want to know which investments are the highest risk.</code> | <code>Which of my investments have the highest risk?</code> | <code>1.0</code> |
| <code>Can you tell me the expected returns on my portfolio?</code> | <code>What is the expected return of my portfolio?</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: 1,844 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: 11.35 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.19 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>Show me how much I have in X</code> | <code>Show my X exposure</code> | <code>1.0</code> |
| <code>Please provide the weekly performance of my portfolio.</code> | <code>What is the performance of my portfolio over the last week?</code> | <code>1.0</code> |
| <code>I want to know my asset allocation.</code> | <code>What is my asset allocation?</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 | 116 | - | 0.9028 |
| 2.0 | 232 | - | 0.9149 |
| 3.0 | 348 | - | 0.9263 |
| 4.0 | 464 | - | 0.9335 |
| 4.3103 | 500 | 0.2052 | 0.9352 |
| 5.0 | 580 | - | 0.9361 |
| 6.0 | 696 | - | 0.9385 |
| 7.0 | 812 | - | 0.9437 |
| 8.0 | 928 | - | 0.9468 |
### 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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