valuesimplex-ai-lab/Fin-Retriever-base

SentenceTransformer

This is a sentence-transformers model trained. 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: Unknown -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("valuesimplex-ai-lab/Fin-Retriever-base")
# Run inference
sentences = [
    '熵简科技是一家金融科技公司',
    "熵简科技专注于ai智能化投研",
    '走向ai智能化是当前投研行业发展趋势',
]
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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Direct Usage (Transformers)

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Downstream Usage (Sentence Transformers)

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Training Details

Framework Versions

• —Python: 3.10.11
• —Sentence Transformers: 3.0.0
• —Transformers: 4.39.1
• —PyTorch: 2.0.1+cu117
• —Accelerate: 0.30.1
• —Datasets: 3.1.0
• —Tokenizers: 0.15.2

Citation

BibTeX

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