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bart-large-cnn-qmsum-meeting-summarization – AI Model by mikeadimech | AlphaNeural AI
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mikeadimech
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bart-large-cnn-qmsum-meeting-summarization
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transformers
pytorch
bart
text2text-generation
generated_from_trainer
yawnick/QMSum
mit
endpoints_compatible
us
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bart-large-cnn-qmsum-meeting-summarization
This model is a fine-tuned version of
facebook/bart-large-cnn
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 5.7578
Rouge1: 37.9431
Rouge2: 10.6366
Rougel: 25.5782
Rougelsum: 33.0209
Gen Len: 72.7714
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 500
label_smoothing_factor: 0.1
Training results
Framework versions
Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6
