metadata

tags:
  - summarization
  - generated_from_trainer
datasets:
  - samsum
metrics:
  - rouge
model-index:
  - name: ssr-base-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: 46.7505
            name: Rouge1
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - type: rouge
            value: 46.2529
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 21.3374
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 36.1939
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 42.2937
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 2.0463898181915283
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 31.3724
            name: gen_len
            verified: true
            verifyToken: >-
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ssr-base-finetuned-samsum-en

This model is a fine-tuned version of microsoft/ssr-base on the samsum dataset. It achieves the following results on the evaluation set:

Loss: 1.6231
Rouge1: 46.7505
Rouge2: 22.3968
Rougel: 37.1784
Rougelsum: 42.891

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
1.9682	1.0	300	1.6432	44.2182	20.8486	35.0914	40.9852
1.6475	2.0	600	1.5946	45.3919	21.6955	36.2411	41.8532
1.5121	3.0	900	1.5737	46.1769	22.4178	36.9762	42.6614
1.4112	4.0	1200	1.5774	46.6047	22.8227	37.2457	43.1935
1.323	5.0	1500	1.5825	46.6162	22.485	37.2846	42.9834
1.2613	6.0	1800	1.5883	46.4253	22.1199	37.0491	42.5189
1.2077	7.0	2100	1.5965	46.485	22.3636	37.2677	42.7499
1.1697	8.0	2400	1.6174	46.8654	22.6291	37.4201	43.0875
1.1367	9.0	2700	1.6188	46.707	22.305	37.156	42.9087
1.118	10.0	3000	1.6231	46.7505	22.3968	37.1784	42.891

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1