ayjays132
/

EnhancerModel

@@ -7,30 +7,67 @@ library_name: transformers
 from transformers import BartTokenizer, BartForConditionalGeneration
 from datasets import load_dataset
-# Load pre-trained BART model for summarization
 tokenizer = BartTokenizer.from_pretrained('ayjays132/EnhancerModel')
 model = BartForConditionalGeneration.from_pretrained('ayjays132/EnhancerModel')
-# Load dataset
 dataset = load_dataset("cnn_dailymail", "3.0.0")
-# Function to generate summary
 def summarize(text):
     inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
     summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
     return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-# Debugging: Print the type and content of the first example
 print("Type of dataset['test']:", type(dataset['test']))
 print("Type of the first element in dataset['test']:", type(dataset['test'][0]))
 print("Content of the first element in dataset['test']:", dataset['test'][0])
-# Test the model on a few examples
 for example in dataset['test'][:5]:
     try:
         # If the example is a string, then it's likely that 'dataset['test']' is not loaded as expected
@@ -47,3 +84,5 @@ for example in dataset['test'][:5]:
                 print("No 'article' field found in this example.")
     except Exception as e:
         print(f"Error processing example: {e}")

+<style>
+    body {
+        font-family: Arial, sans-serif;
+        line-height: 1.6;
+        margin: 0;
+        padding: 20px;
+        background-color: #f4f4f4;
+    }
+    .model-description {
+        background: #fff;
+        border-radius: 8px;
+        padding: 20px;
+        box-shadow: 0 0 10px rgba(0,0,0,0.1);
+        max-width: 800px;
+        margin: auto;
+    }
+    .model-description h2 {
+        margin-top: 0;
+        color: #333;
+    }
+    .model-description p {
+        margin: 0 0 10px;
+        color: #666;
+    }
+</style>
+</head>
+<body>
+<div class="model-description">
+    <h2>Enhanced Model Features</h2>
+    <p><strong>Adaptability:</strong> Adjusts to diverse contexts and user needs, ensuring relevant and precise interactions.</p>
+    <p><strong>Contextual Intelligence:</strong> Provides contextually aware responses, improving engagement and interaction quality.</p>
+    <p><strong>Advanced Algorithms:</strong> Employs cutting-edge algorithms for sophisticated and intelligent responses.</p>
+    <p><strong>User Experience:</strong> Designed with a focus on seamless interaction, offering an intuitive and refined user experience.</p>
+</div>
+<div class="code-container">
+    <code>
 from transformers import BartTokenizer, BartForConditionalGeneration
 from datasets import load_dataset
+Load pre-trained BART model for summarization
 tokenizer = BartTokenizer.from_pretrained('ayjays132/EnhancerModel')
 model = BartForConditionalGeneration.from_pretrained('ayjays132/EnhancerModel')
+Load dataset
 dataset = load_dataset("cnn_dailymail", "3.0.0")
+Function to generate summary
 def summarize(text):
     inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
     summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
     return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+ebugging: Print the type and content of the first example
 print("Type of dataset['test']:", type(dataset['test']))
 print("Type of the first element in dataset['test']:", type(dataset['test'][0]))
 print("Content of the first element in dataset['test']:", dataset['test'][0])
+Test the model on a few examples
 for example in dataset['test'][:5]:
     try:
         # If the example is a string, then it's likely that 'dataset['test']' is not loaded as expected
                 print("No 'article' field found in this example.")
     except Exception as e:
         print(f"Error processing example: {e}")
+    </code>
+</div>