metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: mt5-small-finetuned-amazon-en-es
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: amazon_reviews_multi
          type: amazon_reviews_multi
          config: en
          split: test
        metrics:
          - type: rouge
            value: 16.0953
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 8.4515
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 15.5619
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 15.6211
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 3.2149159908294678
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 11.4986
            name: gen_len
            verified: true
            verifyToken: >-
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mt5-small-finetuned-amazon-en-es

This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.0294
Rouge1: 16.6807
Rouge2: 8.0004
Rougel: 16.2251
Rougelsum: 16.1743

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
6.5928	1.0	1209	3.3005	14.7863	6.5038	14.3031	14.2522
3.9024	2.0	2418	3.1399	16.9257	8.6583	16.15	16.1299
3.5806	3.0	3627	3.0869	18.2734	9.1667	17.7441	17.5782
3.4201	4.0	4836	3.0590	17.763	8.9447	17.1833	17.1661
3.3202	5.0	6045	3.0598	17.7754	8.5695	17.4139	17.2653
3.2436	6.0	7254	3.0409	16.8423	8.1593	16.5392	16.4297
3.2079	7.0	8463	3.0332	16.8991	8.1574	16.4229	16.3515
3.1801	8.0	9672	3.0294	16.6807	8.0004	16.2251	16.1743

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1