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---
library_name: transformers
license: apache-2.0
base_model: google/long-t5-tglobal-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: long_t5_6
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. -->
# long_t5_6
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0450
- Rouge1: 0.5157
- Rouge2: 0.3356
- Rougel: 0.4671
- Rougelsum: 0.4673
- Gen Len: 31.344
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 250 | 1.6173 | 0.4644 | 0.29 | 0.4269 | 0.4268 | 25.406 |
| 2.1255 | 2.0 | 500 | 1.5596 | 0.4748 | 0.2986 | 0.4353 | 0.4354 | 26.834 |
| 2.1255 | 3.0 | 750 | 1.5241 | 0.4819 | 0.3074 | 0.4424 | 0.4423 | 25.6985 |
| 1.7318 | 4.0 | 1000 | 1.5178 | 0.4925 | 0.3161 | 0.4521 | 0.4521 | 26.513 |
| 1.7318 | 5.0 | 1250 | 1.5178 | 0.4975 | 0.3184 | 0.4555 | 0.4555 | 27.042 |
| 1.5463 | 6.0 | 1500 | 1.5168 | 0.5014 | 0.3255 | 0.4614 | 0.4618 | 25.815 |
| 1.5463 | 7.0 | 1750 | 1.5066 | 0.5054 | 0.3306 | 0.4653 | 0.4654 | 25.8755 |
| 1.4053 | 8.0 | 2000 | 1.5184 | 0.508 | 0.3311 | 0.4673 | 0.4673 | 26.246 |
| 1.4053 | 9.0 | 2250 | 1.5372 | 0.5095 | 0.3331 | 0.4669 | 0.4667 | 27.511 |
| 1.289 | 10.0 | 2500 | 1.5446 | 0.5078 | 0.3328 | 0.4662 | 0.4664 | 27.14 |
| 1.289 | 11.0 | 2750 | 1.5500 | 0.5111 | 0.3329 | 0.4687 | 0.4687 | 27.444 |
| 1.191 | 12.0 | 3000 | 1.5660 | 0.5141 | 0.3345 | 0.4704 | 0.4703 | 27.397 |
| 1.191 | 13.0 | 3250 | 1.5731 | 0.5168 | 0.3389 | 0.4735 | 0.4736 | 27.4535 |
| 1.107 | 14.0 | 3500 | 1.5926 | 0.5158 | 0.3357 | 0.4709 | 0.4708 | 28.82 |
| 1.107 | 15.0 | 3750 | 1.6107 | 0.5158 | 0.3406 | 0.473 | 0.4734 | 28.3135 |
| 1.036 | 16.0 | 4000 | 1.6205 | 0.5187 | 0.3411 | 0.4742 | 0.4744 | 28.9715 |
| 1.036 | 17.0 | 4250 | 1.6467 | 0.5142 | 0.3378 | 0.4701 | 0.4702 | 28.81 |
| 0.9655 | 18.0 | 4500 | 1.6670 | 0.5192 | 0.3426 | 0.4748 | 0.4751 | 28.266 |
| 0.9655 | 19.0 | 4750 | 1.6715 | 0.5154 | 0.3373 | 0.4695 | 0.4694 | 29.8395 |
| 0.9055 | 20.0 | 5000 | 1.6824 | 0.5156 | 0.3388 | 0.4715 | 0.4721 | 28.653 |
| 0.9055 | 21.0 | 5250 | 1.7156 | 0.5164 | 0.3384 | 0.4708 | 0.4712 | 30.2485 |
| 0.8519 | 22.0 | 5500 | 1.7239 | 0.5164 | 0.3404 | 0.4733 | 0.4735 | 28.5295 |
| 0.8519 | 23.0 | 5750 | 1.7292 | 0.5169 | 0.3374 | 0.4716 | 0.4718 | 29.1895 |
| 0.8069 | 24.0 | 6000 | 1.7591 | 0.5168 | 0.3369 | 0.4703 | 0.4707 | 29.9035 |
| 0.8069 | 25.0 | 6250 | 1.7733 | 0.5146 | 0.3355 | 0.4689 | 0.4692 | 29.533 |
| 0.764 | 26.0 | 6500 | 1.7963 | 0.5172 | 0.3388 | 0.4716 | 0.4721 | 30.0075 |
| 0.764 | 27.0 | 6750 | 1.8136 | 0.5173 | 0.3385 | 0.471 | 0.4714 | 29.672 |
| 0.7256 | 28.0 | 7000 | 1.8317 | 0.5153 | 0.3361 | 0.4698 | 0.4702 | 30.5335 |
| 0.7256 | 29.0 | 7250 | 1.8478 | 0.5136 | 0.336 | 0.4686 | 0.469 | 30.654 |
| 0.6901 | 30.0 | 7500 | 1.8709 | 0.5169 | 0.338 | 0.472 | 0.4724 | 29.7215 |
| 0.6901 | 31.0 | 7750 | 1.8733 | 0.5153 | 0.3364 | 0.4694 | 0.4698 | 30.3385 |
| 0.6617 | 32.0 | 8000 | 1.8882 | 0.5137 | 0.3369 | 0.4692 | 0.4692 | 29.8545 |
| 0.6617 | 33.0 | 8250 | 1.9176 | 0.5144 | 0.3354 | 0.4689 | 0.4692 | 30.489 |
| 0.6331 | 34.0 | 8500 | 1.9219 | 0.517 | 0.3391 | 0.472 | 0.4723 | 30.3225 |
| 0.6331 | 35.0 | 8750 | 1.9272 | 0.5146 | 0.3367 | 0.469 | 0.4695 | 30.647 |
| 0.6106 | 36.0 | 9000 | 1.9468 | 0.512 | 0.3329 | 0.4658 | 0.466 | 31.4695 |
| 0.6106 | 37.0 | 9250 | 1.9650 | 0.5143 | 0.3345 | 0.4682 | 0.4685 | 31.2565 |
| 0.5914 | 38.0 | 9500 | 1.9666 | 0.5163 | 0.3367 | 0.4705 | 0.4708 | 30.9375 |
| 0.5914 | 39.0 | 9750 | 1.9788 | 0.5134 | 0.3351 | 0.468 | 0.4683 | 30.297 |
| 0.5722 | 40.0 | 10000 | 1.9985 | 0.5118 | 0.3331 | 0.4659 | 0.4662 | 31.1015 |
| 0.5722 | 41.0 | 10250 | 2.0013 | 0.5137 | 0.3341 | 0.4671 | 0.4676 | 30.8835 |
| 0.5571 | 42.0 | 10500 | 2.0087 | 0.513 | 0.333 | 0.4666 | 0.467 | 31.094 |
| 0.5571 | 43.0 | 10750 | 2.0196 | 0.5155 | 0.3361 | 0.4682 | 0.4684 | 31.0515 |
| 0.5466 | 44.0 | 11000 | 2.0221 | 0.5143 | 0.3349 | 0.4674 | 0.4678 | 31.1495 |
| 0.5466 | 45.0 | 11250 | 2.0275 | 0.5146 | 0.3353 | 0.4672 | 0.4676 | 31.1845 |
| 0.5355 | 46.0 | 11500 | 2.0311 | 0.5134 | 0.3344 | 0.4662 | 0.4665 | 30.9715 |
| 0.5355 | 47.0 | 11750 | 2.0410 | 0.5141 | 0.3345 | 0.4657 | 0.466 | 31.6285 |
| 0.5305 | 48.0 | 12000 | 2.0415 | 0.5154 | 0.3359 | 0.467 | 0.4672 | 31.3345 |
| 0.5305 | 49.0 | 12250 | 2.0424 | 0.5157 | 0.3358 | 0.4677 | 0.4678 | 31.033 |
| 0.5256 | 50.0 | 12500 | 2.0450 | 0.5157 | 0.3356 | 0.4671 | 0.4673 | 31.344 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.2.1
- Datasets 3.0.1
- Tokenizers 0.20.1
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