| --- |
| license: cc-by-4.0 |
| datasets: |
| - J-MADRAL/SearchESCI |
| - J-MADRAL/TrainingData |
| language: |
| - en |
| metrics: |
| - recall |
| - trec_eval |
| base_model: |
| - google-bert/bert-base-uncased |
| pipeline_tag: text-ranking |
| tags: |
| - ReviewSearch |
| library_name: transformers |
| --- |
| |
|
|
| **R-MADRAL** is a BERT-sized multi-aspects dense retriever initialized from [BERT](https://huggingface.co/google-bert/bert-base-uncased) public checkpoint, |
| further pre-trained on e-commerce review data, and fine-tuned on review search retrieval task on the |
| [Search ESCI](https://huggingface.co/datasets/J-MADRAL/SearchESCI) 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 ## |
|
|
| R-MADRAL 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 Search ESCI as an example. |
| queries = [ |
| "cotton summer dress care instructions" |
| ] |
| reviews = [ |
| "Cute, cool and comfy summer dress [...] Hand wash and line drys easily, material is crinkly so no ironing needed. [...]", |
| "Excellent machine JET J-2530 15-Inch 3/4-Horsepower Bench Drill Press. Two common Amazon reviewer complaints about higher-end drill presses [...]" |
| ] |
| |
| # Load the tokenizer and model. |
| tokenizer = transformers.AutoTokenizer.from_pretrained("J-MADRAL/R-MADRAL") |
| model = modeling.BiEncoderModel.from_pretrained("J-MADRAL/R-MADRAL") |
| |
| # Tokenize the input data. |
| q_input = tokenizer(queries, |
| add_special_tokens=True, |
| truncation=True, |
| padding=True, |
| max_length=128, |
| return_tensors="pt") |
| r_input = tokenizer(reviews, |
| 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 |
| r_emb = model(**r_input).pooled_output |
| |
| # Compute similarity scores, using dot product similarity. |
| scores = torch.matmul(q_emb, r_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 ## |
|
|
| #### [Search ESCI](https://huggingface.co/datasets/J-MADRAL/SearchESCI) #### |
|
|
| Model | R@100 | R@500 | MRR | nDCG@10 | nDCG@50 |
| |---|---|---|---|---|--- |
| BM25 | 0.5875 | 0.7288 | 0.2539 | 0.2766 | 0.3101 |
| [DRAGON](https://huggingface.co/facebook/dragon-plus-context-encoder) | 0.5451 | 0.6751 | 0.2347 | 0.2567 | 0.2873 |
| [R-BiBERT](https://huggingface.co/J-MADRAL/R-BiBERT) | 0.5972 | 0.7350 | 0.2602 | 0.2855 | 0.3176 |
| **R-MADRAL**(https://huggingface.co/J-MADRAL/R-MADRAL) | *0.6405* | *0.7626* | *0.2944* | *0.3215* | *0.3541* |
| [J-BiBERT](https://huggingface.co/J-MADRAL/J-BiBERT) | 0.6300 | 0.7593 | 0.2879 | 0.3140 | 0.3474 |
| [J-MADRAL](https://huggingface.co/J-MADRAL/J-MADRAL) | **0.6488** | **0.7729** | **0.3007** | **0.3281** | **0.3611** |
