File size: 3,101 Bytes
c57e5d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
- wer
model-index:
- name: t-5-base-baseline
results: []
---
<!-- 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. -->
# t-5-base-baseline
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1785
- Rouge1: 0.6774
- Rouge2: 0.4106
- Rougel: 0.6163
- Rougelsum: 0.6162
- Wer: 0.4869
- Bleurt: 0.3779
## 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Wer | Bleurt |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:------:|
| No log | 0.13 | 250 | 1.3316 | 0.651 | 0.3769 | 0.5867 | 0.5868 | 0.5217 | 0.3009 |
| 1.7919 | 0.27 | 500 | 1.2776 | 0.6595 | 0.3865 | 0.5963 | 0.5963 | 0.5108 | 0.3009 |
| 1.7919 | 0.4 | 750 | 1.2513 | 0.6635 | 0.393 | 0.6015 | 0.6016 | 0.5039 | 0.3009 |
| 1.3552 | 0.53 | 1000 | 1.2326 | 0.6668 | 0.3967 | 0.6049 | 0.6049 | 0.5008 | 0.3009 |
| 1.3552 | 0.66 | 1250 | 1.2236 | 0.6692 | 0.4 | 0.6073 | 0.6073 | 0.4972 | 0.3314 |
| 1.3074 | 0.8 | 1500 | 1.2118 | 0.6713 | 0.4022 | 0.6094 | 0.6094 | 0.4953 | 0.3314 |
| 1.3074 | 0.93 | 1750 | 1.2022 | 0.6715 | 0.4035 | 0.6106 | 0.6106 | 0.4932 | 0.2798 |
| 1.3037 | 1.06 | 2000 | 1.1972 | 0.6731 | 0.4053 | 0.6118 | 0.6118 | 0.4916 | 0.3771 |
| 1.3037 | 1.2 | 2250 | 1.1909 | 0.675 | 0.4068 | 0.6137 | 0.6136 | 0.4905 | 0.3314 |
| 1.2676 | 1.33 | 2500 | 1.1889 | 0.6761 | 0.4087 | 0.6144 | 0.6144 | 0.4893 | 0.3314 |
| 1.2676 | 1.46 | 2750 | 1.1848 | 0.6763 | 0.4091 | 0.6151 | 0.6151 | 0.4884 | 0.3314 |
| 1.2796 | 1.6 | 3000 | 1.1829 | 0.6771 | 0.4095 | 0.6155 | 0.6155 | 0.488 | 0.3123 |
| 1.2796 | 1.73 | 3250 | 1.1808 | 0.6769 | 0.4101 | 0.6159 | 0.6159 | 0.4876 | 0.3779 |
| 1.2489 | 1.86 | 3500 | 1.1787 | 0.6772 | 0.4105 | 0.6162 | 0.6162 | 0.4869 | 0.3771 |
| 1.2489 | 1.99 | 3750 | 1.1785 | 0.6774 | 0.4106 | 0.6163 | 0.6162 | 0.4869 | 0.3779 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|