metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - amazon_reviews_multi
metrics:
  - rouge
model-index:
  - name: mt5-small-finetuned-amazon-en-es
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: amazon_reviews_multi
          type: amazon_reviews_multi
          config: en
          split: validation
          args: en
        metrics:
          - name: Rouge1
            type: rouge
            value: 19.7803

mt5-small-finetuned-amazon-en-es

This model is a fine-tuned version of google/mt5-small on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set:

Loss: 3.3776
Rouge1: 19.7803
Rouge2: 11.1015
Rougel: 19.2549
Rougelsum: 19.0799

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: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.7004	1.0	514	3.2936	17.2056	9.3595	17.2163	16.8991
2.6472	2.0	1028	3.2995	17.4081	9.9623	17.3287	16.9701
2.6173	3.0	1542	3.3039	17.9581	9.5537	17.7729	17.6022
2.5812	4.0	2056	3.3057	17.438	8.8331	17.1341	17.0436
2.5446	5.0	2570	3.3316	18.6351	10.4851	18.5086	18.2872
2.5133	6.0	3084	3.3255	19.1331	10.3396	18.8557	18.6551
2.4885	7.0	3598	3.3522	18.998	10.1323	18.709	18.5662
2.464	8.0	4112	3.3314	18.8978	10.1726	18.5683	18.3049
2.4431	9.0	4626	3.3508	18.777	10.3018	18.5103	18.2069
2.4092	10.0	5140	3.3520	19.3359	11.2355	19.0265	18.7083
2.4082	11.0	5654	3.3534	19.3633	11.2181	19.0465	18.7726
2.3815	12.0	6168	3.3687	18.7702	10.395	18.5383	18.2271
2.3706	13.0	6682	3.3716	19.0868	10.6534	18.791	18.5447
2.3628	14.0	7196	3.3756	19.1222	10.9791	18.6601	18.5131
2.3518	15.0	7710	3.3831	19.9227	11.0903	19.4883	19.2418
2.3497	16.0	8224	3.3776	19.7803	11.1015	19.2549	19.0799

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3