NohTow/colbert_xml-r-english

SentenceTransformer based on hltcoe/plaidx-large-eng-tdist-mt5xxl-engeng

This is a sentence-transformers model finetuned from hltcoe/plaidx-large-eng-tdist-mt5xxl-engeng. It maps sentences & paragraphs to a 128-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: hltcoe/plaidx-large-eng-tdist-mt5xxl-engeng
• Maximum Sequence Length: 32 tokens
• Output Dimensionality: 128 tokens
• 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

ColBERT(
  (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): LinearProjection({'in_features': 1024, 'out_features': 128, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("NohTow/colbert_xml-r-english")
# 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, 128]

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

Training Details

Framework Versions

• Python: 3.9.19
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.3.1
• Accelerate:
• Datasets: 2.19.2
• Tokenizers: 0.19.1

Citation

BibTeX