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pegasus-large-reddit_tifu-samsum-256

Summarization
Transformers
PyTorch
English
pegasus
text2text-generation
google/pegasus-reddit_tifu
samsum
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metadata
language:
  - en
thumbnail: null
tags:
  - pytorch
  - google/pegasus-reddit_tifu
  - summarization
  - samsum
license: null
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 dataset for conversional summaries.

The initial weigths were from the 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)