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--- |
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language: |
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- en |
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thumbnail: |
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tags: |
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- pytorch |
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- google/pegasus-reddit_tifu |
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- summarization |
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- samsum |
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license: |
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datasets: |
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- samsum |
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metrics: |
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- rouge |
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--- |
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# Samsum Pegasus (Reddit/TIFU) for conversational summaries |
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## Model description |
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Pegasus (Reddit/TIFU) for conversational summaries trained on the samsum dataset! |
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## Training data |
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The data is the [samsum](https://huggingface.co/datasets/samsum) dataset for conversional summaries. |
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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. |
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## Training procedure |
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Used the _example/seq2seq/run_summarization.py_ script from the transformers source _4.5.0dev0_. |
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n_epochs: 3,\ |
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batch_size: 8, \ |
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max_source_length: 256,\ |
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max_target_length: 128 |
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## Eval results |
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eval_gen_len: 35.9939,\ |
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eval_loss: 1.4284523725509644,\ |
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eval_rouge1: 46.5613,\ |
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eval_rouge2: 23.6137,\ |
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eval_rougeL: 37.2397,\ |
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eval_rougeLsum: 42.7126,\ |
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eval_samples_per_second: 4.302 |
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## Example |
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from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
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model_name = "jpcorb20/pegasus-large-reddit_tifu-samsum-256" |
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tokenizer = PegasusTokenizer.from_pretrained(model_name) |
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model = PegasusForConditionalGeneration.from_pretrained(model_name) |
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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""" |
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token_params = dict(max_length=256, truncation=True, padding='longest', return_tensors="pt") |
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batch = tokenizer(src_text, **token_params) |
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translated = model.generate(**batch) |
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decode_params = dict(num_beams=5, min_length=16, max_length=128, length_penalty=2) |
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tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True, **decode_params) |
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print(tgt_text) |
