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t5-small-finetuned-de-en-lr2e-4
rossanez/t5-small-finetuned-de-en-lr2e-4
t5-small-finetuned-de-en-lr2e-4 is a machine learning model from rossanez. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 4, 2021
- SHA
- 1d56f514
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
t5-small-finetuned-de-en-lr2e-4
This model is a fine-tuned version of t5-small on the wmt14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.0115
- Bleu: 9.12
- Gen Len: 17.4026
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 188 | 2.0701 | 8.1225 | 17.4542 |
| No log | 2.0 | 376 | 2.0316 | 8.5741 | 17.4229 |
| 2.2224 | 3.0 | 564 | 2.0229 | 8.9227 | 17.3703 |
| 2.2224 | 4.0 | 752 | 2.0105 | 9.0764 | 17.4053 |
| 2.2224 | 5.0 | 940 | 2.0115 | 9.12 | 17.4026 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Dec 4, 2021