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@@ -5,11 +5,17 @@ datasets:
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  metrics:
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  -rouge1: 0.3156
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  ---
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- <details> <summary><strong>Example usage</strong></summary>
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- ```
 
 
 
 
 
 
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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- tokenizer = AutoTokenizer.from_pretrained("KookyGhost/GPT2-small-summarization")
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  model = AutoModelForCausalLM.from_pretrained("KookyGhost/GPT2-small-summarization")
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  prompt = "Summarize this: Reddit user shares a long story about learning to code with free online resources and eventually landing their first developer job."
@@ -26,5 +32,3 @@ outputs = model.generate(
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  summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  print(summary)
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- ```
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- </details>
 
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  metrics:
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  -rouge1: 0.3156
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  ---
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+ ### Example usage
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+
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+ <p>
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+
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+ </p>
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+ ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("gpt2")
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  model = AutoModelForCausalLM.from_pretrained("KookyGhost/GPT2-small-summarization")
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  prompt = "Summarize this: Reddit user shares a long story about learning to code with free online resources and eventually landing their first developer job."
 
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  summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  print(summary)