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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/ 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


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)



Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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