Downloads · 30 days
3
14% of all-time downloads
my_awesome_billsum_model_34
limaatulya/my_awesome_billsum_model_34
my_awesome_billsum_model_34 is a machine learning model from limaatulya. 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
22
Public
Parameters
60.5M
243 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors242 MB · 99%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Jun 20, 2024
- Updated
- Jun 20, 2024
- SHA
- 12892299
Base models
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. -->
my_awesome_billsum_model_34
This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6615
- Rouge1: 0.9649
- Rouge2: 0.8639
- Rougel: 0.9148
- Rougelsum: 0.916
- Gen Len: 4.7917
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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 12 | 2.2768 | 0.4083 | 0.2813 | 0.3855 | 0.3853 | 17.3333 |
| No log | 2.0 | 24 | 1.7504 | 0.4318 | 0.2948 | 0.3978 | 0.3966 | 16.6042 |
| No log | 3.0 | 36 | 1.2490 | 0.4721 | 0.3506 | 0.4443 | 0.4447 | 15.3542 |
| No log | 4.0 | 48 | 0.9124 | 0.7673 | 0.6558 | 0.7251 | 0.7253 | 9.0833 |
| No log | 5.0 | 60 | 0.7653 | 0.9289 | 0.8292 | 0.8817 | 0.8823 | 5.7292 |
| No log | 6.0 | 72 | 0.7176 | 0.9649 | 0.8639 | 0.9148 | 0.916 | 4.7917 |
| No log | 7.0 | 84 | 0.6921 | 0.9649 | 0.8639 | 0.9148 | 0.916 | 4.7917 |
| No log | 8.0 | 96 | 0.6765 | 0.9649 | 0.8639 | 0.9148 | 0.916 | 4.7917 |
| No log | 9.0 | 108 | 0.6655 | 0.9649 | 0.8639 | 0.9148 | 0.916 | 4.7917 |
| No log | 10.0 | 120 | 0.6615 | 0.9649 | 0.8639 | 0.9148 | 0.916 | 4.7917 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Jun 20, 2024
- Updated
- Jun 20, 2024