README.md

metadata

tags:
  - summarization
  - fa
  - mbert
  - mbert2mbert
  - Abstractive Summarization
  - generated_from_trainer
datasets:
  - pn_summary
model-index:
  - name: mbert2mbert-finetune-fa
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 0
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 0
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 0
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 0
            verified: true
          - name: loss
            type: loss
            value: 11.443371772766113
            verified: true
          - name: gen_len
            type: gen_len
            value: 20
            verified: true

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