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retnet-Final-smallest-summarization – AI Model by kaizerBox | AlphaNeural AI
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kaizerBox
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retnet-Final-smallest-summarization
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transformers
tensorboard
safetensors
retnet
text-generation
generated_from_trainer
xsum
autotrain_compatible
endpoints_compatible
us
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retnet-Final-smallest-summarization
This model is a fine-tuned version of
on the xsum dataset. It achieves the following results on the evaluation set:
Loss: 5.1417
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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
5.8127
1.0
5762
5.3184
5.2364
2.0
11525
5.1682
5.1518
3.0
17286
5.1417
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0
