|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:6300 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Consumer Products segment decreased 10% to $3,572.5 million. |
|
sentences: |
|
- What was the impact of the Federal Reserve’s policy changes on Schwab money market |
|
funds in 2022? |
|
- What was the total revenue of Hasbro's Consumer Products segment in 2022? |
|
- How much did the company's currently payable U.S. taxes amount to in 2023? |
|
- source_sentence: PricewaterhouseCoopers LLP is mentioned as the Firm’s independent |
|
registered public accounting firm (PCAOB ID 238) in the audit of the Consolidated |
|
Financial Statements. |
|
sentences: |
|
- Where in the document can the Consolidated Financial Statements be found as mentioned |
|
in a 2024 report? |
|
- What type of firm is PricewaterhouseCoopers LLP as mentioned in the context of |
|
auditing? |
|
- Which note in the report provides details about legal proceedings? |
|
- source_sentence: If, in the future, foreign exchange or capital control restrictions |
|
were to be imposed and become applicable to us, such restrictions could potentially |
|
reduce the amounts that we would be able to receive from our Macao, Hong Kong |
|
and mainland China subsidiaries. |
|
sentences: |
|
- What are the potential consequences for the parent company if foreign exchange |
|
or capital control restrictions were imposed in the future? |
|
- What is described under Item 8 in the context of a financial document? |
|
- What types of investments are primarily included in the Goldman Sachs' investments |
|
in funds at NAV as of December 2023? |
|
- source_sentence: Determining income tax provisions involves forecasting future financial |
|
results, planning potential tax strategies, and evaluating the probability of |
|
sustaining tax positions against audits. |
|
sentences: |
|
- What type of company is Johnson & Johnson described as? |
|
- What determines the fair value of available-for-sale short-term investments? |
|
- What factors influence the determination of income tax provisions and related |
|
tax balances? |
|
- source_sentence: During the fiscal year ended March 31, 2023, a $118 million tax |
|
charge increased the valuation allowance on Swiss deferred tax assets, leading |
|
to a higher effective tax rate. |
|
sentences: |
|
- What accounted for the significant tax rate increase in fiscal year 2023? |
|
- What percentage of the box office revenue in the U.S./Canada was generated by |
|
the three largest exhibitors in 2023? |
|
- What percentage of eBay's 2023 net revenues were attributed to international markets? |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### 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': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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() |
|
) |
|
``` |
|
|
|
## 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("ValentinaKim/bge-base-financial-matryoshka4") |
|
# Run inference |
|
sentences = [ |
|
'During the fiscal year ended March 31, 2023, a $118 million tax charge increased the valuation allowance on Swiss deferred tax assets, leading to a higher effective tax rate.', |
|
'What accounted for the significant tax rate increase in fiscal year 2023?', |
|
'What percentage of the box office revenue in the U.S./Canada was generated by the three largest exhibitors in 2023?', |
|
] |
|
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] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## 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.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 6,300 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 2 tokens</li><li>mean: 46.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.35 tokens</li><li>max: 51 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>For the year ended December 31, 2023, net cash used in financing activities included $1.8 billion for dividends to GM, which are eliminated within the consolidated statements of cash flows.</code> | <code>What amount of dividends to GM were included in the net cash used in financing activities for GM Financial for the year ended December 31, 2023?</code> | |
|
| <code>Assets and liabilities of these foreign entities are translated at exchange rates in effect as of the balance sheet date.</code> | <code>At what values are assets and liabilities of foreign entities translated in financial statements?</code> | |
|
| <code>The 21st Century Cures Act broadened patient access to certain enhanced benefits offered by Medicare Advantage plans, increasing the percentage of patients on these plans.</code> | <code>How did the 21st Century Cures Act affect patient access to Medicare Advantage plans?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
