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mt5-small_large_lr
psxjp5/mt5-small_large_lr
mt5-small_large_lr is a machine learning model from psxjp5. 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
- mt5
- Access
- Public
- Created
- Aug 5, 2023
- Updated
- Aug 5, 2023
- SHA
- 9dcae902
Base models
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. -->
mt5-small_large_lr
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: 0.9688
- Rouge1: 38.8633
- Rouge2: 33.0802
- Rougel: 37.6956
- Rougelsum: 37.7116
- Bleu: 26.6301
- Gen Len: 11.5566
- Meteor: 0.3519
- No ans accuracy: 22.99
- Av cosine sim: 0.6861
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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 9
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | No ans accuracy | Av cosine sim |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5.4434 | 1.0 | 175 | 2.1918 | 1.8449 | 1.2024 | 1.7039 | 1.7116 | 0.0 | 2.7672 | 0.0145 | 28.9700 | 0.1363 |
| 1.8436 | 1.99 | 350 | 1.1852 | 33.6062 | 26.8725 | 32.2258 | 32.241 | 20.3395 | 12.2528 | 0.2957 | 17.3800 | 0.636 |
| 1.2276 | 2.99 | 525 | 1.0630 | 33.186 | 27.4949 | 32.0715 | 32.0522 | 20.3232 | 11.0301 | 0.2957 | 21.18 | 0.6109 |
| 0.9589 | 3.98 | 700 | 1.0083 | 40.265 | 33.6652 | 38.9503 | 38.9661 | 28.0884 | 12.8545 | 0.3623 | 17.54 | 0.7157 |
| 0.7931 | 4.98 | 875 | 0.9682 | 37.9437 | 31.7611 | 36.7618 | 36.7671 | 25.7738 | 12.0286 | 0.3424 | 20.66 | 0.6825 |
| 0.6686 | 5.97 | 1050 | 0.9601 | 37.5742 | 31.9098 | 36.4225 | 36.4381 | 24.9584 | 11.4169 | 0.3398 | 22.56 | 0.6713 |
| 0.5686 | 6.97 | 1225 | 0.9620 | 43.1436 | 36.6363 | 41.7279 | 41.7571 | 32.4301 | 13.6142 | 0.3893 | 16.9400 | 0.757 |
| 0.4939 | 7.96 | 1400 | 0.9688 | 38.8633 | 33.0802 | 37.6956 | 37.7116 | 26.6301 | 11.5566 | 0.3519 | 22.99 | 0.6861 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Aug 5, 2023
- Updated
- Aug 5, 2023