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: 8, \
    max_source_length: 256,\
    max_target_length: 128
  
  ## Eval results
  
      eval_gen_len: 35.9939,\
      eval_loss: 1.4284523725509644,\
      eval_rouge1: 46.5613,\
      eval_rouge2: 23.6137,\
      eval_rougeL: 37.2397,\
      eval_rougeLsum: 42.7126,\
      eval_samples_per_second: 4.302
      
  ## 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\nAlexis: Thanks Carter. Yeah, I really enjoyed myself as well.\r\nCarter: If you are up for it, I would really like to see you again soon.\r\nAlexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\r\nCarter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\r\nAlexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\r\n"""
      
      token_params = dict(max_length=256, 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)