Downloads · 30 days
8
11% of all-time downloads
my_awesome_billsum_model
tihonn/my_awesome_billsum_model
my_awesome_billsum_model is a machine learning model from tihonn. 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
75
Public
Parameters
223M
6.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors892 MB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- May 6, 2025
- Updated
- May 6, 2025
- SHA
- b4559a2f
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
This model is a fine-tuned version of t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.0543
- Rouge1: 0.2075
- Rouge2: 0.0585
- Rougel: 0.1472
- Rougelsum: 0.1473
- Gen Len: 19.7264
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 133 | 5.5755 | 0.2491 | 0.0692 | 0.165 | 0.1651 | 16.6189 |
| No log | 2.0 | 266 | 5.4112 | 0.3096 | 0.0881 | 0.2061 | 0.206 | 17.0981 |
| No log | 3.0 | 399 | 5.3456 | 0.3484 | 0.1 | 0.2263 | 0.2265 | 17.8396 |
| 5.6693 | 4.0 | 532 | 5.2749 | 0.355 | 0.1081 | 0.2322 | 0.2323 | 17.7811 |
| 5.6693 | 5.0 | 665 | 5.1996 | 0.3662 | 0.1102 | 0.2374 | 0.2376 | 18.2943 |
| 5.6693 | 6.0 | 798 | 5.7658 | 0.3352 | 0.097 | 0.2194 | 0.2196 | 19.6679 |
| 5.6693 | 7.0 | 931 | 6.0595 | 0.2208 | 0.062 | 0.1555 | 0.1555 | 19.7245 |
| 5.8498 | 8.0 | 1064 | 6.0556 | 0.2137 | 0.0603 | 0.1514 | 0.1516 | 19.7226 |
| 5.8498 | 9.0 | 1197 | 6.0549 | 0.2087 | 0.059 | 0.1481 | 0.1482 | 19.734 |
| 5.8498 | 10.0 | 1330 | 6.0550 | 0.2087 | 0.0591 | 0.1479 | 0.1479 | 19.734 |
| 5.8498 | 11.0 | 1463 | 6.0550 | 0.2087 | 0.059 | 0.1478 | 0.1479 | 19.734 |
| 6.5293 | 12.0 | 1596 | 6.0550 | 0.2088 | 0.059 | 0.1479 | 0.148 | 19.7264 |
| 6.5293 | 13.0 | 1729 | 6.0549 | 0.2089 | 0.0591 | 0.1479 | 0.148 | 19.734 |
| 6.5293 | 14.0 | 1862 | 6.0548 | 0.2088 | 0.0591 | 0.1479 | 0.148 | 19.734 |
| 6.5293 | 15.0 | 1995 | 6.0546 | 0.2092 | 0.0592 | 0.148 | 0.148 | 19.734 |
| 6.5241 | 16.0 | 2128 | 6.0545 | 0.2087 | 0.059 | 0.1478 | 0.1479 | 19.734 |
| 6.5241 | 17.0 | 2261 | 6.0544 | 0.2088 | 0.0591 | 0.1479 | 0.148 | 19.734 |
| 6.5241 | 18.0 | 2394 | 6.0543 | 0.2075 | 0.0585 | 0.1472 | 0.1473 | 19.7264 |
| 6.5259 | 19.0 | 2527 | 6.0543 | 0.2075 | 0.0585 | 0.1472 | 0.1473 | 19.7264 |
| 6.5259 | 20.0 | 2660 | 6.0543 | 0.2075 | 0.0585 | 0.1472 | 0.1473 | 19.7264 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- May 6, 2025
- Updated
- May 6, 2025