metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
base_model: BookingCare/gte-multilingual-base-onnx-quantized
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on BookingCare/gte-multilingual-base-onnx-quantized

This is a sentence-transformers model finetuned from BookingCare/gte-multilingual-base-onnx-quantized. 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: BookingCare/gte-multilingual-base-onnx-quantized
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 8192, 'do_lower_case': False}) with Transformer model: ORTModelForFeatureExtraction 
  (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:

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("BookingCare/gte-multilingual-base-onnx-quantized")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
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]

Training Details

Framework Versions

Python: 3.10.15
Sentence Transformers: 3.3.1
Transformers: 4.46.3
PyTorch: 2.1.2+cpu
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

BookingCare
/

gte-multilingual-base-onnx-quantized