README.md

metadata

tags:
  - summarization
  - fa
  - mbert
  - mbert2mbert
  - Abstractive Summarization
  - generated_from_trainer
datasets:
  - pn_summary
model-index:
  - name: mbert2mbert-finetune-fa
    results: []

mbert2mbert-finetune-fa

This model is a fine-tuned version of on the pn_summary dataset.

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• 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 4.19.4
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1