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mbart-finetune-en-cnn – AI Model by eslamxm | AlphaNeural AI
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eslamxm
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mbart-finetune-en-cnn
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transformers
pytorch
tensorboard
mbart
text2text-generation
summarization
en
seq2seq
Abstractive Summarization
generated_from_trainer
cnn_dailymail
endpoints_compatible
us
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mbert-finetune-en-cnn
This model is a fine-tuned version of
facebook/mbart-large-50
on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
Loss: 3.5577
Rouge-1: 37.69
Rouge-2: 16.47
Rouge-l: 35.53
Gen Len: 79.93
Bertscore: 74.92
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: 0.0005
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
num_epochs: 5
label_smoothing_factor: 0.1
Training results
Framework versions
Transformers 4.20.0
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1
