File size: 2,721 Bytes
46a46c7 0a375ee 46a46c7 0a375ee 46a46c7 8785abd 46a46c7 c43503d 46a46c7 0a375ee 46a46c7 0a375ee 46a46c7 0a375ee |
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 83 84 85 86 87 88 89 |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.2001
---
<!-- 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. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1970
- Rouge1: 0.2001
- Rouge2: 0.1053
- Rougel: 0.1716
- Rougelsum: 0.1717
- Gen Len: 19.0
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 124 | 2.5355 | 0.1414 | 0.0544 | 0.1183 | 0.1182 | 19.0 |
| No log | 2.0 | 248 | 2.3807 | 0.1674 | 0.0738 | 0.1416 | 0.1412 | 19.0 |
| No log | 3.0 | 372 | 2.3128 | 0.1977 | 0.1007 | 0.1695 | 0.1697 | 19.0 |
| No log | 4.0 | 496 | 2.2729 | 0.1987 | 0.1008 | 0.1695 | 0.1694 | 19.0 |
| 2.8078 | 5.0 | 620 | 2.2460 | 0.1997 | 0.1025 | 0.1707 | 0.1707 | 19.0 |
| 2.8078 | 6.0 | 744 | 2.2251 | 0.2011 | 0.1034 | 0.1715 | 0.1714 | 19.0 |
| 2.8078 | 7.0 | 868 | 2.2133 | 0.2016 | 0.1049 | 0.172 | 0.172 | 19.0 |
| 2.8078 | 8.0 | 992 | 2.2035 | 0.2018 | 0.1062 | 0.1723 | 0.1725 | 19.0 |
| 2.4762 | 9.0 | 1116 | 2.1985 | 0.2008 | 0.1059 | 0.172 | 0.1723 | 19.0 |
| 2.4762 | 10.0 | 1240 | 2.1970 | 0.2001 | 0.1053 | 0.1716 | 0.1717 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|