Bge Base Financial Matryoshka

🚀 BGE base Financial Matryoshka

This model, based on sentence-transformers, is fine - tuned from BAAI/bge-base-en-v1.5. It maps sentences and paragraphs to a 768 - dimensional dense vector space and can be applied in various tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.

✨ Features

• Maps sentences and paragraphs to a 768 - dimensional vector space.
• Suitable for multiple NLP tasks like semantic similarity, search, and classification.

📦 Installation

First, install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("philschmid/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "What was Gilead's total revenue in 2023?",
    'What was the total revenue for the year ended December 31, 2023?',
    'How much was the impairment related to the CAT loan receivable 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]

📚 Documentation

Model Details

Model Description

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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()
)

🔧 Technical Details

Evaluation

Information Retrieval

The model is evaluated on multiple datasets using the InformationRetrievalEvaluator.

Dataset: basline_768

Dataset: basline_512

Dataset: basline_256

Dataset: basline_128

Dataset: basline_64

Training Details

Training Dataset

Unnamed Dataset

• Size: 6,300 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 10 tokens mean: 46.11 tokens max: 289 tokens	min: 7 tokens mean: 20.26 tokens max: 43 tokens

• Samples:

  positive	anchor
  Fiscal 2023 total gross profit margin of 35.1% represents an increase of 1.7 percentage points as compared to the respective prior year period.	What was the total gross profit margin for Hewlett Packard Enterprise in fiscal 2023?
  Noninterest expense increased to $65.8 billion in 2023, primarily due to higher investments in people and technology and higher FDIC expense, including $2.1 billion for the estimated special assessment amount arising from the closure of Silicon Valley Bank and Signature Bank.	What was the total noninterest expense for the company in 2023?
  As of May 31, 2022, FedEx Office had approximately 12,000 employees.	How many employees did FedEx Office have as of May 31, 2022?

📄 License

This model is released under the apache - 2.0 license.