Downloads · 30 days
8
3% of all-time downloads
flan-t5-small-samsum
Wazzzabeee/flan-t5-small-samsum
flan-t5-small-samsum is a machine learning model from Wazzzabeee. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
300
Public
Parameters
77M
309 MB on disk
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors308 MB · 99%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Dec 19, 2023
- Updated
- Dec 19, 2023
- SHA
- 2f752411
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
flan-t5-small-samsum
This model is a fine-tuned version of google/flan-t5-small on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.6729
- Rouge1: 42.6222
- Rouge2: 18.682
- Rougel: 35.3954
- Rougelsum: 38.9104
- Gen Len: 16.9170
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.8863 | 0.22 | 100 | 1.7049 | 42.1145 | 18.0254 | 34.733 | 38.4052 | 16.5788 |
| 1.8463 | 0.43 | 200 | 1.6947 | 42.4119 | 18.2925 | 34.9702 | 38.8535 | 17.3614 |
| 1.8548 | 0.65 | 300 | 1.6792 | 42.5967 | 18.5244 | 35.1965 | 38.9087 | 17.1514 |
| 1.8358 | 0.87 | 400 | 1.6772 | 42.167 | 18.2032 | 34.8647 | 38.4144 | 16.5873 |
| 1.8129 | 1.08 | 500 | 1.6729 | 42.6222 | 18.682 | 35.3954 | 38.9104 | 16.9170 |
| 1.8068 | 1.3 | 600 | 1.6709 | 42.5238 | 18.311 | 35.1257 | 38.6584 | 16.9451 |
| 1.7973 | 1.52 | 700 | 1.6687 | 42.8715 | 18.6133 | 35.3054 | 38.971 | 16.7546 |
| 1.7979 | 1.74 | 800 | 1.6668 | 42.9038 | 18.7483 | 35.4156 | 39.1118 | 16.8791 |
| 1.7899 | 1.95 | 900 | 1.6670 | 43.1142 | 18.7369 | 35.4796 | 39.2724 | 16.9109 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Dec 19, 2023
- Updated
- Dec 19, 2023