File size: 2,000 Bytes
6ff0d70 |
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 |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetuned-scitldr_param2
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. -->
# t5-base-finetuned-scitldr_param2
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: 3.1205
- Rouge1: 23.543
- Rouge2: 10.3655
- Rougel: 19.6492
- Rougelsum: 20.8429
- Gen Len: 18.9822
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.98 | 1.0 | 996 | 3.1319 | 23.1445 | 10.0902 | 19.3585 | 20.4683 | 18.9758 |
| 1.9833 | 2.0 | 1992 | 3.1284 | 23.6157 | 10.3862 | 19.7155 | 20.8825 | 18.9822 |
| 1.9078 | 3.0 | 2988 | 3.1219 | 23.7444 | 10.5443 | 19.8405 | 21.0207 | 18.9774 |
| 1.907 | 4.0 | 3984 | 3.1199 | 23.6053 | 10.414 | 19.7221 | 20.9581 | 18.9822 |
| 1.8561 | 5.0 | 4980 | 3.1205 | 23.543 | 10.3655 | 19.6492 | 20.8429 | 18.9822 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|