readme: ---
  language: 
  - en
  thumbnail: 
  tags:
  - pytorch
  - google/pegasus-reddit_tifu
  - summarization
  - samsum
  license: 
  datasets:
  - samsum
  metrics:
  - rouge
  ---
  
  # Samsum Pegasus (Reddit/TIFU) for conversational summaries
  
  ## Model description
  
  Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!
  
  ## Training data
  
  The data is the [samsum](https://huggingface.co/datasets/samsum) dataset for conversional summaries.
  
  The initial weigths were from the [google/pegasus-reddit_tifu](https://huggingface.co/google/pegasus-reddit_tifu). The hypothesis being that it would help the convergence on the samsum dataset to have weights trained on a larger summarization dataset first like the Reddit TIFU using casual language.
  
  ## Training procedure
  
  Used the example/seq2seq/run_summarization.py script from the transformers source 4.5.0dev0.
  
    n_epochs: 3,\
    batch_size: 4, \
    max_source_length: 512,\
    max_target_length: 128
  
  ## Eval results
    
    eval_gen_len: 35.89,\
    eval_loss: 1.3807392120361328,\
    eval_rouge1: 47.3372,\
    eval_rouge2: 24.4728,\
    eval_rougeL: 37.9078,\
    eval_rougeLsum: 43.5744,\
    eval_samples_per_second: 2.814
    
  ## Example
  
    from transformers import PegasusForConditionalGeneration, PegasusTokenizer
    
    model_name = "jpcorb20/pegasus-large-reddit_tifu-samsum-256"
    
    tokenizer = PegasusTokenizer.from_pretrained(model_name)
    model = PegasusForConditionalGeneration.from_pretrained(model_name)
    
    src_text = """Carter: Hey Alexis, I just wanted to let you know that I had a really nice time with you tonight.\\r\
    Alexis: Thanks Carter. Yeah, I really enjoyed myself as well.\\r\
    Carter: If you are up for it, I would really like to see you again soon.\\r\
    Alexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\\r\
    Carter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\\r\
    Alexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\\r\
    """
    
    token_params = dict(max_length=512, truncation=True, padding='longest', return_tensors="pt")
    batch = tokenizer(src_text, **token_params)
    
    translated = model.generate(**batch)
    
    decode_params = dict(num_beams=5, min_length=16, max_length=128, length_penalty=2)
    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True, **decode_params)
    
    print(tgt_text)