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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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