paligemma2-3b-pt-448-jax
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View details ↗paligemma2-3b-pt-896
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View details ↗paligemma2-3b-pt-896-jax
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View details ↗PALM-2
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View details ↗path-foundation
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View details ↗pegasus-aeslc
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View details ↗pegasus-arxiv
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View details ↗pegasus-big_patent
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View details ↗pegasus-billsum
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View details ↗pegasus-cnn_dailymail
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View details ↗pegasus-gigaword
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View details ↗pegasus-large
GoogleWe train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.
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View details ↗pegasus-multi_news
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View details ↗pegasus-newsroom
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View details ↗pegasus-pubmed
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View details ↗pegasus-reddit_tifu
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View details ↗pegasus-wikihow
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View details ↗pegasus-x-base
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View details ↗pegasus-x-base-arxiv
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View details ↗pegasus-xsum
GoogleSee Docs: here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.
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View details ↗pix2struct-ai2d-base
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