File size: 2,187 Bytes
818b21e 154a30d 818b21e 154a30d 818b21e 154a30d 818b21e 154a30d 818b21e 154a30d 818b21e |
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 |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- govreport-summarization
metrics:
- rouge
model-index:
- name: govreport-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: govreport-summarization
type: govreport-summarization
config: document
split: train[:17000]
args: document
metrics:
- name: Rouge1
type: rouge
value: 0.1673
---
<!-- 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. -->
# govreport-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the govreport-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2117
- Rouge1: 0.1673
- Rouge2: 0.0792
- Rougel: 0.1398
- Rougelsum: 0.1398
- 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6565 | 1.0 | 850 | 2.3189 | 0.164 | 0.0744 | 0.1364 | 0.1365 | 19.0 |
| 2.3913 | 2.0 | 1700 | 2.2522 | 0.1656 | 0.0766 | 0.1379 | 0.138 | 19.0 |
| 2.2813 | 3.0 | 2550 | 2.2187 | 0.1669 | 0.0779 | 0.1393 | 0.1394 | 19.0 |
| 2.2273 | 4.0 | 3400 | 2.2117 | 0.1673 | 0.0792 | 0.1398 | 0.1398 | 19.0 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|