taisetaise/poc-st-transformer-model-args

SentenceTransformer based on sshleifer/tiny-distilbert-base-cased

This is a sentence-transformers model finetuned from sshleifer/tiny-distilbert-base-cased. It maps sentences & paragraphs to a None-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: sshleifer/tiny-distilbert-base-cased <!-- at revision 657df2b83a6986d88e4f528740259c9b49f796b1 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: None dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': False, 'architecture': 'BertModel'})
)

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("sentence_transformers_model_id")
# 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, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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Downstream Usage (Sentence Transformers)

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Training Details

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.3.0
• —Transformers: 4.57.1
• —PyTorch: 2.8.0
• —Accelerate:
• —Datasets: 4.0.0
• —Tokenizers: 0.22.1

Citation

BibTeX

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