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bart-fine-tuned-for-text-summarization – AI Model by vapit | AlphaNeural AI
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vapit
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bart-fine-tuned-for-text-summarization
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transformers
tensorboard
safetensors
bart
text2text-generation
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 2.4789
Model Preparation Time: 0.0018
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: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Model Preparation Time
2.6951
1.0
157
2.4949
0.0018
2.5552
2.0
314
2.4694
0.0018
2.5356
3.0
471
2.4909
0.0018
Framework versions
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0
