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LA1512/result

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README.md ADDED
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+ ---
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+ license: bsd-3-clause
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+ base_model: LA1512/led-1000-epoch-1
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - pubmed-summarization
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: results
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+ results:
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+ - task:
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+ name: Sequence-to-sequence Language Modeling
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+ type: text2text-generation
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+ dataset:
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+ name: pubmed-summarization
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+ type: pubmed-summarization
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+ config: section
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+ split: validation
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+ args: section
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+ metrics:
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+ - name: Rouge1
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+ type: rouge
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+ value: 43.1934
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # results
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+
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+ This model is a fine-tuned version of [LA1512/led-1000-epoch-1](https://huggingface.co/LA1512/led-1000-epoch-1) on the pubmed-summarization dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.1831
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+ - Rouge1: 43.1934
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+ - Rouge2: 16.7702
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+ - Rougel: 24.2151
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+ - Rougelsum: 38.4858
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+ - Gen Len: 267.815
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 1
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+ - label_smoothing_factor: 0.1
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.1.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
config.json ADDED
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+ "activation_dropout": 0.0,
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+ "activation_function": "gelu",
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+ "LEDForConditionalGeneration"
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+ ],
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+ }
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tokenizer.json ADDED
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