---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---

Distilbert encoder models trained on Amazon product-to-product recommendation dataset (LF-AmazonTitles-1.3M) using the [DEXML](https://github.com/nilesh2797/DEXML) ([Dual Encoder for eXtreme Multi-Label classification, ICLR'24](https://arxiv.org/pdf/2310.10636v2.pdf)) method.

## Inference Usage (Sentence-Transformers)
With `sentence-transformers` installed you can use this model as following:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('quicktensor/dexml_lf-amazontitles-1.3m')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
With huggingface transformers you only need to be a bit careful with how you pool the transformer output to get the embedding, you can use this model as following;
```python
from transformers import AutoTokenizer, AutoModel

pooler = lambda x: x[:, 0, :] # Choose CLS token

sentences = ["This is an example sentence", "Each sentence is converted"]
tokenizer = AutoTokenizer.from_pretrained('quicktensor/dexml_lf-amazontitles-1.3m')
model = AutoModel.from_pretrained('quicktensor/dexml_lf-amazontitles-1.3m')

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
    embeddings = pooler(model(**encoded_input))

print(embeddings)
```

## Cite
If you found this model helpful, please cite our work as:
```bib
@InProceedings{DEXML,
  author    = "Gupta, N. and Khatri, D. and Rawat, A-S. and Bhojanapalli, S. and Jain, P. and Dhillon, I.",
  title     = "Dual-encoders for Extreme Multi-label Classification",
  booktitle = "International Conference on Learning Representations",
  month     = "May",
  year      = "2024"
}
```