Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset!
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.
Used the example/seq2seq/run_summarization.py script from the transformers source 4.5.0dev0.
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
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)
Select AutoNLP in the “Train” menu to fine-tune this model automatically.
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