Update README.md

README.md

@@ -31,6 +31,63 @@ This repository contains a fine-tuned model for generating high-quality product
 
 Developed by team at https://exnrt.com
 
+## Usage
+
+T5 model expects a task related prefix: since it is a description generation task, we will add a prefix "description: "
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Ateeqq/product-description-generator", token="your_token")
+model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/product-description-generator", token="your_token")
+
+input_text = "18-Piece Kitchen Dinnerware white Set, Plates, Dishes, Bowls"
+
+inputs = tokenizer.encode("description: " + input_text, return_tensors="pt", max_length=128, truncation=True)
+
+outputs = model.generate(inputs, max_length=400, num_beams=2, num_beam_groups=2, num_return_sequences=2, repetition_penalty=1.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.9, early_stopping=True)
+
+description = tokenizer.decode(outputs[1], skip_special_tokens=True)
+
+print(description)
+```
+
+## Getting Multiple Outputs
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+device = "cuda"
+tokenizer = AutoTokenizer.from_pretrained("Ateeqq/product-description-generator", token='your_token')
+model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/product-description-generator", token='your_token').to(device)
+
+def generate_description(title):
+    input_ids = tokenizer(f'description: {title}', return_tensors="pt", padding="longest", truncation=True, max_length=128).input_ids.to(device)
+    outputs = model.generate(
+        input_ids,
+        num_beams=5,
+        num_beam_groups=5,
+        num_return_sequences=5,
+        repetition_penalty=10.0,
+        diversity_penalty=3.0,
+        no_repeat_ngram_size=2,
+        temperature=0.7,
+        max_length=128
+    )
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+title = '18-Piece Kitchen Dinnerware white Set, Plates, Dishes, Bowls'
+generate_description(title)
+```
+### Outputs:
+```
+['18-Piece Kitchen Dinnerware White Set, Plates, Dishes and Bowls. This set includes a large bowl with two plates for serving dinner or dessert dishes in the kitchen. The white plate is made of durable stainless steel that will not fade over time.',
+ 'The 18 Piece Kitchen Dinnerware Set features a white plate, dish and bowl. This set is made of durable stainless steel with an elegant design that will add elegance to your kitchen.',
+ 'This 18-piece dinnerware set is made of durable stainless steel. It features a white finish and comes with an easy to clean handle for ease of cleaning. The bowls are dishwasher safe, microwave safe or can be used as tablecloths in the kitchen.',
+ 'Kitchen Dinnerware is a great addition to your kitchen. This 18-piece set includes four plates, two dishes and three bowls for serving food or beverages in the dining room with an elegant design that will add sophistication to any tabletop setting.',
+ "p>This 18-piece dinnerware set is made of high quality stainless steel. The white plate and dish are dishwasher safe, easy to clean with the included lids for ease of use. This dining table set features an elegant design that will add elegance style in your kitchen or living room. It's also perfect for serving food on any occasion like birthday parties, house warming ceremonies, Thanksgiving celebrations etc."]
+```
+
 ## Features
 
 - **Architecture**: t5-base (223M parameters)
@@ -60,26 +117,6 @@ Developed by team at https://exnrt.com
 - **Batch Size**: 1
 - **Max Epochs**: 1
 
-## Usage
-
-# Load model directly
-
-```python
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-tokenizer = AutoTokenizer.from_pretrained("Ateeqq/product-description-generator", token="your_token")
-model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/product-description-generator", token="your_token")
-
-input_text = "SAMSUNG Galaxy S23+ Series AI Phone, Unlocked Android Smartphone, 256GB Storage, 8GB RAM, 50MP Camera"
-
-inputs = tokenizer.encode("description: " + input_text, return_tensors="pt", max_length=50, truncation=True)
-
-outputs = model.generate(inputs, max_length=400, num_beams=2, num_beam_groups=2, num_return_sequences=2, repetition_penalty=1.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.9, early_stopping=True)
-
-description = tokenizer.decode(outputs[1], skip_special_tokens=True)
-
-print(description)
-```
 ## Future Work
 
 - **Update Training Data**: Retrain the model using the latest 0.5 million cleaned examples.