README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- en
---

# Fin_Embed_Large

This is a finetune of BAAI/bge-large-en-v1.5. It is finetuned on Q/A pairs from ~ 50 s&p 500 annual reports. 

## Usage (Sentence-Transformers)

To use this model [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```



## Evaluation Results

Evaluated on retrieval task using financial documents held out from training data.

| Model        | cos_sim-Accuracy@1 | cos_sim-Accuracy@3 | cos_sim-Accuracy@5 | cos_sim-Accuracy@10 | cos_sim-Precision@1 | cos_sim-Recall@1 | cos_sim-Precision@3 | cos_sim-Recall@3 | cos_sim-Precision@5 | cos_sim-Recall@5 | cos_sim-Precision@10 | cos_sim-Recall@10 |
|--------------|--------------------|--------------------|--------------------|---------------------|---------------------|------------------|---------------------|------------------|---------------------|------------------|---------------------|------------------|
| BGE Large 1.5| 0.513663092        | 0.698374265        | 0.771359391        | 0.849878935         | 0.513663092         | 0.513663092      | 0.232791422         | 0.698374265      | 0.154271878         | 0.771359391      | 0.084987893          | 0.849878935      |
| FIN_EMBED    | 0.592182636        | 0.7741266          | 0.833275683        | 0.89346247          | 0.592182636         | 0.592182636      | 0.2580422           | 0.7741266        | 0.166655137         | 0.833275683      | 0.089346247          | 0.89346247       |


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 443 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 2,
    "evaluation_steps": 50,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 88,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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()
)
```

## Citing & Authors

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