File size: 3,125 Bytes
1baba0c |
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-bertsum-500
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-bertsum-500
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: 1.2994
- Rouge1: 0.6466
- Rouge2: 0.3657
- Rougel: 0.5798
- Rougelsum: 0.5798
- Wer: 0.5246
- Bleurt: -0.0784
## 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 | 1.4553 | 0.6223 | 0.3344 | 0.552 | 0.552 | 0.557 | -0.4294 |
| 1.9648 | 0.27 | 500 | 1.3993 | 0.6301 | 0.3443 | 0.5613 | 0.5614 | 0.5467 | -0.4022 |
| 1.9648 | 0.4 | 750 | 1.3747 | 0.6341 | 0.35 | 0.5661 | 0.5661 | 0.5402 | -0.3802 |
| 1.4858 | 0.53 | 1000 | 1.3547 | 0.638 | 0.3533 | 0.5693 | 0.5693 | 0.5378 | -0.0447 |
| 1.4858 | 0.66 | 1250 | 1.3431 | 0.639 | 0.3559 | 0.5715 | 0.5715 | 0.5342 | -0.0292 |
| 1.4484 | 0.8 | 1500 | 1.3321 | 0.6406 | 0.3578 | 0.573 | 0.573 | 0.5322 | -0.0292 |
| 1.4484 | 0.93 | 1750 | 1.3238 | 0.6418 | 0.3593 | 0.5747 | 0.5747 | 0.5306 | -0.0784 |
| 1.4226 | 1.06 | 2000 | 1.3185 | 0.6433 | 0.3616 | 0.5762 | 0.5762 | 0.5281 | -0.1084 |
| 1.4226 | 1.2 | 2250 | 1.3131 | 0.6442 | 0.3624 | 0.5775 | 0.5775 | 0.5277 | -0.1084 |
| 1.3917 | 1.33 | 2500 | 1.3102 | 0.6453 | 0.3638 | 0.5783 | 0.5783 | 0.5266 | -0.0784 |
| 1.3917 | 1.46 | 2750 | 1.3060 | 0.6458 | 0.3641 | 0.5788 | 0.5788 | 0.5256 | -0.0292 |
| 1.4048 | 1.6 | 3000 | 1.3040 | 0.6461 | 0.3649 | 0.5792 | 0.5792 | 0.5253 | -0.0784 |
| 1.4048 | 1.73 | 3250 | 1.3015 | 0.6463 | 0.3653 | 0.5796 | 0.5795 | 0.525 | -0.0292 |
| 1.3803 | 1.86 | 3500 | 1.2999 | 0.6463 | 0.3654 | 0.5795 | 0.5795 | 0.5247 | -0.0784 |
| 1.3803 | 1.99 | 3750 | 1.2994 | 0.6466 | 0.3657 | 0.5798 | 0.5798 | 0.5246 | -0.0784 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|