Update README.md

README.md

@@ -14,4 +14,97 @@ base_model:
 pipeline_tag: text-ranking
 tags:
 - ProductSearch
----
+---
+
+
+**P-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 product data, and fine-tuned on product search retrieval task on the 
+[Amazon ESCI](https://huggingface.co/datasets/J-MADRAL/AmazonESCI) dataset. 
+We use symmetric encoder architecture, with a single shared encoder for both queries and products.
+The similarity function is *dot product*.
+
+
+## Paper and Repository ##
+
+P-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 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-MADRAL")
+model = modeling.BiEncoderModel.from_pretrained("J-MADRAL/P-MADRAL")
+
+# 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](https://huggingface.co/J-MADRAL/P-BiBERT) | 0.6018 | 0.7640 | 0.4939 | 0.3276 | 0.3967
+**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](https://huggingface.co/J-MADRAL/P-BiBERT) | 0.7432 | 0.8757 | 0.8256 | 0.6612 | 0.6287
+**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*