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--- |
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license: apache-2.0 |
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library_name: transformers |
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<body> |
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<div class="model-description"> |
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<h2>Enhanced Model Features</h2> |
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<p><strong>Adaptability:</strong> Adjusts to diverse contexts and user needs, ensuring relevant and precise interactions.</p> |
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<p><strong>Contextual Intelligence:</strong> Provides contextually aware responses, improving engagement and interaction quality.</p> |
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<p><strong>Advanced Algorithms:</strong> Employs cutting-edge algorithms for sophisticated and intelligent responses.</p> |
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<p><strong>User Experience:</strong> Designed with a focus on seamless interaction, offering an intuitive and refined user experience.</p> |
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</div> |
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<body> |
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<div class="code-container"> |
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<pre><code> |
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from transformers import BartTokenizer, BartForConditionalGeneration |
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from datasets import load_dataset |
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# Load pre-trained BART model for summarization |
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tokenizer = BartTokenizer.from_pretrained('ayjays132/EnhancerModel') |
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model = BartForConditionalGeneration.from_pretrained('ayjays132/EnhancerModel') |
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# Load dataset |
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dataset = load_dataset("cnn_dailymail", "3.0.0") |
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# Function to generate summary |
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def summarize(text): |
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inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True) |
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summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) |
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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# Debugging: Print the type and content of the first example |
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print("Type of dataset['test']:", type(dataset['test'])) |
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print("Type of the first element in dataset['test']:", type(dataset['test'][0])) |
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print("Content of the first element in dataset['test']:", dataset['test'][0]) |
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# Test the model on a few examples |
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for example in dataset['test'][:5]: |
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try: |
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# If the example is a string, then it's likely that 'dataset['test']' is not loaded as expected |
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if isinstance(example, str): |
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print(f"Article: {example}\n") |
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print(f"Summary: {summarize(example)}\n") |
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else: |
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# Access the 'article' field if the example is a dictionary |
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article = example.get('article', None) |
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if article: |
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print(f"Article: {article}\n") |
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print(f"Summary: {summarize(article)}\n") |
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else: |
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print("No 'article' field found in this example.") |
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except Exception as e: |
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print(f"Error processing example: {e}") |
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</code></pre> |
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</div> |
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</body> |
