Update README.md

README.md

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----
-license: mit
----
+---
+library_name: transformers
+tags:
+- recommendation
+- retrieval
+- two-tower
+- prag
+- pytorch
+- modernbert
+- amazon-reviews
+---
+
+# PRAG Encoder Model
+
+This is a PRAG (Prompting for RAG-styled recommendation) encoder model trained on Amazon review data. It uses a two-tower architecture with a shared ModernBERT backbone to embed both user queries and product items into a common vector space for efficient retrieval.
+
+## Model Details
+
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+The PRAG model is designed for product recommendation and retrieval tasks. It maps text inputs (queries or item descriptions) to high-dimensional embeddings. The model is optimized using contrastive learning, where the goal is to maximize the cosine similarity between a query and its corresponding relevant item while minimizing similarity with irrelevant items.
+
+- **Developed by:** @[dodo](https://huggingface.co/do2do2), @[quocdat32461997](https://huggingface.co/tendatngo)
+- **Model type:** LLM Encoder
+- **Language(s) (NLP):** English
+- **Finetuned from model:** LLM Encoder
+
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/do2do2-ai
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be used directly for:
+- Semantic search in e-commerce catalogs.
+- Building recommendation systems by matching user intent (queries) to product descriptions.
+- Zero-shot product retrieval tasks.
+
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Non-English text (not explicitly trained/validated).
+- General-purpose text embedding outside the e-commerce/recommendation domain.
+- Tasks requiring generative capabilities (this is an encoder-only model).
+
+## Bias, Risks, and Limitations
+
+- **Domain Specificity:** Optimized for product data with similar features and characteristics, but performance may vary on different domains or datasets.
+- **Language:** Limited to English.
+- **Biases:** May inherit biases present in the Amazon Reviews 2023 dataset.
+
+### Recommendations
+
+Users should evaluate the model on their specific product domain before deployment. Consider fine-tuning if the target domain's vocabulary differs significantly from Amazon's.
+
+## How to Get Started with the Model
+
+You can use the model via the recommendation API. Below is an example of how to make an authenticated request to get product recommendations.
+
+```python
+import os
+import requests
+import json
+
+# Configuration
+api_key = "YOUR_API_KEY"
+recommend_url = "https://trydodo.xyz" 
+
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {api_key}",
+}
+
+# Define context, template, and product catalog
+payload = {
+    "context": {
+        "previous_purchases": ["electronics", "books"],
+        "budget": 100.0,
+    },
+    "template": "Recommend next product to customer. Previous purchases: {previous_purchases}, Budget less than: {budget}",
+    "catalog": {
+        "product_1": "Wireless headphones - Premium noise-cancelling",
+        "product_2": "Laptop stand - Adjustable aluminum ergonomic",
+        "product_3": "Python book - Complete guide for beginners",
+        "product_4": "Smartphone case - Shockproof cover with kickstand",
+        "product_5": "USB-C hub - 7-in-1 adapter with 4K HDMI",
+    },
+}
+
+try:
+    response = requests.post(
+        url=f"{recommend_url}/api/recommend/recommend",
+        params={
+            "model_key": "prag_v1",
+            "num_results": 5,
+        },
+        headers=headers,
+        json=payload,
+    )
+    response.raise_for_status()
+    
+    recommendations = response.json()
+    print(f"Recommendations: {json.dumps(recommendations, indent=2)}")
+
+except Exception as e:
+    print(f"Request failed: {e}")
+```
+
+## Training Details
+
+The model was trained following standard large language model (LLM) training practices for encoder-based retrieval systems.
+
+### Training Data
+
+The model is trained on diverse product-related datasets, including product reviews, metadata, and user interaction logs. The data is preprocessed to emphasize semantic relationships between queries and items.
+
+### Training Procedure
+
+#### Preprocessing
+
+- Standard tokenization using a ModernBERT-based tokenizer.
+- Input sequences are formatted to represent user intent (queries) and product characteristics (items).
+- Dynamic padding and truncation are applied to optimize training efficiency.
+
+#### Training Hyperparameters
+
+The training follows casual LLM training practices:
+- **Batch Size:** Scaled to maximize GPU utilization.
+- **Loss Function:** Contrastive learning objective.
+- **Temperature:** Tuned to balance retrieval precision and recall.
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Metrics
+
+The model is evaluated using standard retrieval metrics:
+- Precision@K (K=2, 5, 10, 20)
+- Recall@K (K=2, 5, 10, 20)
+- Hit Ratio@K (K=2, 5, 10, 20)
+- NDCG@K (K=2, 5, 10, 20)
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- **Backbone:** Standard transformer-based encoder for text representation.
+- **Objective:** Contrastive learning objective
+
+### Software
+
+- **Framework:** PyTorch
+- **Library:** Transformers
+
+## Model Card Authors
+
+quocdat32461997