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flan-t5-large-stacked-samsum-1024

Summarization
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PyTorch
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Safetensors
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t5
text2text-generation
Generated from Trainer
stacked summaries
prompt engineering
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text-generation-inference
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flan-t5-large-stacked-samsum-1024 / README.md
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pszemraj
Add evaluation results on the samsum config and test split of samsum (#1)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - summarization
metrics:
  - rouge
datasets:
  - stacked-summaries/stacked-samsum-1024
model-index:
  - name: flan-t5-large-stacked-samsum1024-WIP3
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 47.6682
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 23.3053
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 39.7678
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 43.259
            verified: true
          - name: loss
            type: loss
            value: 2.372586965560913
            verified: true
          - name: gen_len
            type: gen_len
            value: 17.4237
            verified: true

flan-t5-large-stacked-samsum1024-WIP3

This model is a fine-tuned version of pszemraj/flan-t5-large-stacked-samsum1024-WIP2 on the stacked-summaries/stacked-samsum-1024 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1311
  • Rouge1: 58.1114
  • Rouge2: 29.339
  • Rougel: 44.7611
  • Rougelsum: 54.2823
  • Gen Len: 122.364

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: 0.0006
  • train_batch_size: 4
  • eval_batch_size: 2
  • seed: 2760
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 256
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.02
  • num_epochs: 2.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
0.1734 1.0 115 1.8751 57.9286 29.2743 44.7181 54.2295 122.123
0.1098 2.0 230 2.1311 58.1114 29.339 44.7611 54.2823 122.364

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.6.1
  • Tokenizers 0.13.1