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metadata
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
  - translation
  - generated_from_trainer
metrics:
  - bleu
model-index:
  - name: t5-small-finetuned-chinese-to-hausa
    results: []

t5-small-finetuned-chinese-to-hausa

This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6584
  • Bleu: 12.1339
  • Gen Len: 17.7108

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: 0.0008
  • train_batch_size: 32
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 5000
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Bleu Gen Len
3.7025 1.0 846 2.6105 10.8813 18.9939
2.5979 2.0 1692 2.1871 11.8977 18.9845
2.2642 3.0 2538 1.9964 10.0898 18.9515
2.0674 4.0 3384 1.8804 10.6493 17.1889
1.943 5.0 4230 1.8018 10.3424 18.9035
1.8501 6.0 5076 1.7529 12.523 18.8649
1.7736 7.0 5922 1.7176 11.9247 18.8803
1.711 8.0 6768 1.6823 11.623 18.3937
1.6537 9.0 7614 1.6550 12.3403 18.8995
1.6083 10.0 8460 1.6398 11.774 18.6873
1.5689 11.0 9306 1.6300 12.8081 18.8652
1.5314 12.0 10152 1.6279 12.2929 18.854
1.4962 13.0 10998 1.6197 12.1522 17.8497
1.4655 14.0 11844 1.6050 12.0764 17.9718
1.435 15.0 12690 1.6076 12.2447 17.9524
1.4047 16.0 13536 1.6048 11.4017 18.6209
1.38 17.0 14382 1.6134 11.5516 18.7405
1.3513 18.0 15228 1.6105 8.6492 14.2507
1.3282 19.0 16074 1.6174 12.094 17.7717
1.3052 20.0 16920 1.6239 11.5085 18.7053
1.2835 21.0 17766 1.6238 12.1588 17.7876
1.2653 22.0 18612 1.6339 12.0899 17.7112
1.2503 23.0 19458 1.6399 12.1466 17.7452
1.2357 24.0 20304 1.6461 9.0097 14.3578
1.2236 25.0 21150 1.6501 12.2617 17.7143
1.2139 26.0 21996 1.6528 12.1118 17.7233
1.2066 27.0 22842 1.6576 12.1435 17.7094
1.2037 28.0 23688 1.6568 12.1328 17.7133
1.2 29.0 24534 1.6581 12.1289 17.7109
1.1967 30.0 25380 1.6584 12.1339 17.7108

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1