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flan-t5-large-stacked-samsum-1024

Summarization
Transformers
PyTorch
ONNX
Safetensors
English
t5
text2text-generation
Generated from Trainer
stacked summaries
prompt engineering
Eval Results
text-generation-inference
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - summarization
  - stacked summaries
  - prompt engineering
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
language:
  - en
library_name: transformers
pipeline_tag: summarization
widget: null
parameters:
  max_length: 128
  min_length: 4
  num_beams: 8
  repetition_penalty: 1.21
  length_penalty: 1
  early_stopping: true

flan-t5-large-stacked-samsum-1024

Open In Colab

This model is a fine-tuned version of google/flan-t5-large on the stacked-summaries/stacked-samsum-1024 dataset.

It achieves the following results on the evaluation set:

  • Loss: 2.1846
  • Rouge1: 57.9637
  • Rouge2: 28.7446
  • Rougel: 44.3826
  • Rougelsum: 54.0399
  • Gen Len: 122.77

Model description

More information needed

Intended uses & limitations

  • max input/output is 1024 tokens
  • this is mostly a test because samsum is not exactly the best dataset for general-purpose summarization

Training and evaluation data

See the dataset card linked on this page for info

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 24915
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.02
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
0.1195 0.17 20 2.0635 57.8829 28.7887 44.4256 54.1299 121.8
0.1084 0.35 40 2.1178 58.0416 28.6487 44.3905 54.1557 122.893
0.1019 0.52 60 2.1576 57.816 28.7069 44.4242 53.9598 120.524
0.0975 0.7 80 2.1821 57.9597 28.8178 44.4854 54.068 121.793
0.0947 0.87 100 2.1846 57.9637 28.7446 44.3826 54.0399 122.77

Framework versions

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