metadata
license: apache-2.0
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
This work aims to improve and measure the ranking performance of information retrieval models, especially for retrieving relevant products given a search query.
Blog post: https://www.marqo.ai/blog/generalized-contrastive-learning-for-multi-modal-retrieval-and-ranking
Paper: https://arxiv.org/pdf/2404.08535.pdf
Text-only
| Methods | Models | nDCG | ERR | RBP |
|---|---|---|---|---|
| BM25 | - | 0.071 | 0.028 | 0.052 |
| E5 | e5-large-v2 | 0.335 | 0.095 | 0.289 |
| E5 (GCL) | e5-large-v2 | 0.470 | 0.457 | 0.374 |
Usage
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: Espresso Pitcher with Handle',
'query: Women’s designer handbag sale',
"passage: Dianoo Espresso Steaming Pitcher, Espresso Milk Frothing Pitcher Stainless Steel",
"passage: Coach Outlet Eliza Shoulder Bag - Black - One Size"]
tokenizer = AutoTokenizer.from_pretrained('Marqo/marqo-gcl-e5-large-v2-130')
model_new = AutoModel.from_pretrained('Marqo/marqo-gcl-e5-large-v2-130')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=77, padding=True, truncation=True, return_tensors='pt')
outputs = model_new(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())