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README.md
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# Overview
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the
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It achieves the following results on the evaluation set:
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- eval_runtime: 820.6405
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- eval_samples_per_second: 2.463
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- eval_steps_per_second: 0.617
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- step: 0
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps:
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- total_train_batch_size:
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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_ratio: 0.1
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- Transformers 4.21.0.dev0
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- Pytorch 1.10.0
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- Datasets 2.
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- Tokenizers 0.12.1
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# Overview
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the allenai/mslr2022 ms2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.7602
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- Rouge1 Fmeasure Mean: 28.5338
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- Rouge2 Fmeasure Mean: 9.5060
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- Rougel Fmeasure Mean: 20.9321
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- Rougelsum Fmeasure Mean: 24.0998
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- Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.21.0.dev0)-rescaled_fast-tokenizer
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- Bertscore F1 Mean: 22.7619
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- Seed: 42
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- Model Name Or Path: allenai/led-base-16384
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- Doc Sep Token: </s>
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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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_ratio: 0.1
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- Transformers 4.21.0.dev0
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- Pytorch 1.10.0
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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