You can deploy this model and start earning money today!

endre01/
infonce-e5-small-v2-0614-0450-st

Name: infonce-e5-small-v2-0614-0450-st
Availability: InStock

sentence-transformers

endre01/infonce-e5-small-v2-0614_0450

finetune

autotrain_compatible

text-embeddings-inference

endpoints_compatible

Views

No views yet

SentenceTransformer based on endre01/infonce-e5-small-v2-0614_0450

This is a sentence-transformers model finetuned from endre01/infonce-e5-small-v2-0614_0450. It maps sentences & paragraphs to a 384-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: endre01/infonce-e5-small-v2-0614_0450
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 512, 'do_lower_case': False}) with Transformer model: 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})
)

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("endre01/infonce-e5-small-v2-0614-0450-st")
# 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, 384]

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

Training Details

Framework Versions

Python: 3.12.8
Sentence Transformers: 4.0.2
Transformers: 4.51.3
PyTorch: 2.6.0+cu126
Accelerate: 1.3.0
Datasets: 3.6.0
Tokenizers: 0.21.0