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retnet-summarization – AI Model by kaizerBox | AlphaNeural AI | AlphaNeural AI
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kaizerBox
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retnet-summarization
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transformers
tensorboard
safetensors
retnet
text-generation
generated_from_trainer
xsum
kaizerBox/retnet-summarization
finetune
autotrain_compatible
endpoints_compatible
us
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retnet-summarization
This model is a fine-tuned version of
kaizerBox/retnet-summarization
on the xsum dataset. It achieves the following results on the evaluation set:
Loss: 3.1397
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: 0.001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
3.4307
1.0
11525
3.3046
3.2601
2.0
23050
3.1760
3.1144
3.0
34575
3.1397
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0
