metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
datasets:
  - samsum
metrics:
  - rouge
model-index:
  - name: t5-small-finetuned-samsum-en
    results:
      - task:
          type: text2text-generation
          name: Sequence-to-sequence Language Modeling
        dataset:
          name: samsum
          type: samsum
          args: samsum
        metrics:
          - type: rouge
            value: 44.3313
            name: Rouge1
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - type: rouge
            value: 40.0386
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 15.8501
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 31.8084
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 36.0888
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 2.1917073726654053
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 18.1074
            name: gen_len
            verified: true
            verifyToken: >-
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t5-small-finetuned-samsum-en

This model is a fine-tuned version of t5-small on the samsum dataset. It achieves the following results on the evaluation set:

Loss: 1.9335
Rouge1: 44.3313
Rouge2: 20.71
Rougel: 37.221
Rougelsum: 40.9603

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: 5.6e-05
train_batch_size: 10
eval_batch_size: 10
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.4912	1.0	300	1.9043	44.1517	20.0186	36.6053	40.5164
1.5055	2.0	600	1.8912	44.1473	20.4456	37.069	40.6714
1.4852	3.0	900	1.8986	44.7536	20.8646	37.525	41.2189
1.4539	4.0	1200	1.9136	44.2144	20.3446	37.1088	40.7581
1.4262	5.0	1500	1.9215	44.2656	20.6044	37.3267	40.9469
1.4118	6.0	1800	1.9247	43.8793	20.4663	37.0614	40.6065
1.3987	7.0	2100	1.9256	43.9981	20.2703	36.7856	40.6354
1.3822	8.0	2400	1.9316	43.9732	20.4559	36.8039	40.5784
1.3773	9.0	2700	1.9314	44.3075	20.5435	37.0457	40.832
1.3795	10.0	3000	1.9335	44.3313	20.71	37.221	40.9603

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1