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rabaevn/
m1-beauty-identity

Name: m1-beauty-identity
Rating: 2.5 (31 reviews)

sentence-transformers

text-embeddings-inference

endpoints_compatible

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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

Maximum Sequence Length: 256 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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): M1FusedHead(
    (W_behav): Linear(in_features=384, out_features=384, bias=False)
    (W_pref): Linear(in_features=384, out_features=384, bias=False)
  )
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("rabaevn/m1-beauty-identity")
# 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)
# tensor([[1.0000, 0.7693, 0.3292],
#         [0.7693, 1.0000, 0.3313],
#         [0.3292, 0.3313, 1.0000]])

Training Details

Framework Versions

Python: 3.10.18
Sentence Transformers: 5.1.0
Transformers: 5.6.2
PyTorch: 2.10.0+cu128
Accelerate: 1.10.0
Datasets: 4.1.1
Tokenizers: 0.22.2