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--- |
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license: openrail |
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inference: |
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parameters: |
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temperature: 0.7 |
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max_length: 24 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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tags: |
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- text-generation-inference |
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widget: |
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- text: 'generate title: Importance, Dataset, AI' |
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example_title: Example 1 |
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- text: 'generate title: Amazon, Product, Business' |
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example_title: Example 2 |
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- text: 'generate title: History, Computer, Software' |
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example_title: Example 3 |
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--- |
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,,,python |
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def generate_title(keywords): |
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input_ids = tokenizer(keywords, return_tensors="pt", padding="longest", truncation=True, max_length=32).input_ids.to(device) |
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outputs = model.generate( |
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input_ids, |
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num_beams=3, |
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num_beam_groups=3, |
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num_return_sequences=3, |
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repetition_penalty=7.0, |
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diversity_penalty=4.0, |
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no_repeat_ngram_size=3, |
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temperature=0.9, |
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max_length=32 |
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) |
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return tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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keywords = 'This repository contains a fine-tuned model for generating high-quality product descriptions.' |
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generate_title(keywords) |
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,,, |
