Declutr Small

🚀 DeCLUTR-small

The "DeCLUTR-small" model is designed for sentence similarity tasks, offering a powerful solution for semantic understanding in text.

🚀 Quick Start

The "DeCLUTR-small" model is sourced from our paper: DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. It is intended to serve as a universal sentence encoder, similar to Google's Universal Sentence Encoder or Sentence Transformers.

✨ Features

• Universal Encoder: Can be used as a universal sentence encoder.
• Semantic Similarity: Capable of computing semantic similarity between text sentences.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

With SentenceTransformers

from scipy.spatial.distance import cosine
from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer("johngiorgi/declutr-small")

# Prepare some text to embed
texts = [
    "A smiling costumed woman is holding an umbrella.",
    "A happy woman in a fairy costume holds an umbrella.",
]

# Embed the text
embeddings = model.encode(texts)

# Compute a semantic similarity via the cosine distance
semantic_sim = 1 - cosine(embeddings[0], embeddings[1])

With 🤗 Transformers

import torch
from scipy.spatial.distance import cosine
from transformers import AutoModel, AutoTokenizer

# Load the model
tokenizer = AutoTokenizer.from_pretrained("johngiorgi/declutr-small")
model = AutoModel.from_pretrained("johngiorgi/declutr-small")

# Prepare some text to embed
text = [
    "A smiling costumed woman is holding an umbrella.",
    "A happy woman in a fairy costume holds an umbrella.",
]
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")

# Embed the text
with torch.no_grad():
    sequence_output = model(**inputs)[0]

# Mean pool the token-level embeddings to get sentence-level embeddings
embeddings = torch.sum(
    sequence_output * inputs["attention_mask"].unsqueeze(-1), dim=1
) / torch.clamp(torch.sum(inputs["attention_mask"], dim=1, keepdims=True), min=1e-9)

# Compute a semantic similarity via the cosine distance
semantic_sim = 1 - cosine(embeddings[0], embeddings[1])

📚 Documentation

Please see our repo for full details.

📄 License

This model is licensed under the apache-2.0 license.

BibTeX entry and citation info

@inproceedings{giorgi-etal-2021-declutr,
	title        = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations},
	author       = {Giorgi, John  and Nitski, Osvald  and Wang, Bo  and Bader, Gary},
	year         = 2021,
	month        = aug,
	booktitle    = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
	publisher    = {Association for Computational Linguistics},
	address      = {Online},
	pages        = {879--895},
	doi          = {10.18653/v1/2021.acl-long.72},
	url          = {https://aclanthology.org/2021.acl-long.72}
}