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license: cc-by-4.0
datasets:
- J-MADRAL/AmazonESCI
- J-MADRAL/TREC_Product_Search_2023
- J-MADRAL/TrainingData
language:
- en
metrics:
- recall
- trec_eval
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-ranking
tags:
- ProductSearch
library_name: transformers
---
**P-BiBERT** is a BERT-sized dense retriever initialized from [BERT](https://huggingface.co/google-bert/bert-base-uncased) public checkpoint,
further pre-trained on e-commerce product data, and fine-tuned on product search retrieval task on the
[Amazon ESCI](https://huggingface.co/datasets/J-MADRAL/AmazonESCI) dataset.
It uses a symmetric encoder architecture, with a single shared encoder for both queries and products.
The similarity function is *dot product*.
## Paper and Repository ##
P-BiBERT has been described in the *Multi-Aspect Joint Retrieval for E-Commerce: Bridging Product Catalogs and Customer Reviews* paper.
The associated GitHub repository is available at [https://anonymous.4open.science/r/J-MADRAL-C4CC](https://anonymous.4open.science/r/J-MADRAL-C4CC).
## Usage (HuggingFace Transformers) ##
Using the model directly in HuggingFace transformers requires additional code available in the [repository](https://anonymous.4open.science/r/J-MADRAL-C4CC).
```python
import modeling
import torch
import transformers
# We use a training query from Amazon ESCI as an example.
queries = [
"iphone 11 pro max case"
]
products = [
"OtterBox Symmetry Series Case for iPhone 11 Pro Max - Black [...]",
"Camera Lens Protector for iPhone 11 Pro/Pro Max, Tempered Glass 9H Hardness Anti-Scratch Camera Screen Protective [...]"
]
# Load the tokenizer and model.
tokenizer = transformers.AutoTokenizer.from_pretrained("J-MADRAL/P-BiBERT")
model = modeling.BiEncoderModel.from_pretrained("J-MADRAL/P-BiBERT")
# Tokenize the input data.
q_input = tokenizer(queries,
add_special_tokens=True,
truncation=True,
padding=True,
max_length=128,
return_tensors="pt")
p_input = tokenizer(products,
add_special_tokens=True,
truncation=True,
padding=True,
max_length=128,
return_tensors="pt")
# Compute embeddings: take the "pooled_output".
q_emb = model(**q_input).pooled_output
p_emb = model(**p_input).pooled_output
# Compute similarity scores, using dot product similarity.
scores = torch.matmul(q_emb, p_emb.transpose(0, 1))
```
## Training Hyperparameters ##
Training Stage | Num. Epochs | Learning Rate | AP Scaling Factor | Max Num Tokens | Batch Size | Num Negatives
|---|---|---|---|---|---|---
Pre-training | 20 | 1e-4 | 0.10 | 128 | 64 | ---
Fine-tuning | 20 | 5e-6 | 0.05 | 128 | 64 | 7
The data used for fine-tuning is available at [https://huggingface.co/datasets/J-MADRAL/TrainingData](https://huggingface.co/datasets/J-MADRAL/TrainingData).
## Evaluation Results ##
#### [Amazon ESCI](https://huggingface.co/datasets/J-MADRAL/AmazonESCI) ####
Model | R@100 | R@500 | MRR | nDCG@10 | nDCG@50
|---|---|---|---|---|---
BM25 | 0.4949 | 0.6603 | 0.4056 | 0.2599 | 0.3160
[DRAGON](https://huggingface.co/facebook/dragon-plus-context-encoder) | 0.5490 | 0.7155 | 0.4541 | 0.2929 | 0.3540
**P-BiBERT** | 0.6018 | 0.7640 | 0.4939 | 0.3276 | 0.3967
[P-MADRAL](https://huggingface.co/J-MADRAL/P-MADRAL) | **0.6235** | **0.7806** | **0.5060** | **0.3382** | **0.4104**
[J-BiBERT](https://huggingface.co/J-MADRAL/J-BiBERT) | 0.5889 | 0.7556 | 0.4808 | 0.3195 | 0.3858
[J-MADRAL](https://huggingface.co/J-MADRAL/J-MADRAL) | *0.6083* | *0.7729* | *0.4988* | *0.3330* | *0.4028*
#### [TREC Product Search 2023](https://huggingface.co/datasets/J-MADRAL/TREC_Product_Search_2023) ####
Model | R@100 | R@500 | MRR | nDCG@10 | nDCG@50
|---|---|---|---|---|---
BM25 | 0.7252 | 0.8650 | 0.7746 | 0.6231 | 0.5988
[DRAGON](https://huggingface.co/facebook/dragon-plus-context-encoder) | 0.7373 | 0.8692 | 0.8135 | 0.6526 | 0.6293
**P-BiBERT** | 0.7432 | 0.8757 | 0.8256 | 0.6612 | 0.6287
[P-MADRAL](https://huggingface.co/J-MADRAL/P-MADRAL) | **0.7547** | *0.8840* | **0.8337** | **0.6719** | **0.6480**
[J-BiBERT](https://huggingface.co/J-MADRAL/J-BiBERT) | 0.7432 | 0.8684 | 0.8114 | 0.6487 | 0.6213
[J-MADRAL](https://huggingface.co/J-MADRAL/J-MADRAL) | *0.7527* | **0.8888** | *0.8333* | *0.6702* | *0.6411* |