Downloads · 30 days
10
20% of all-time downloads
longt5_xl_summ_screen_memsum_bp_30
learn3r/longt5_xl_summ_screen_memsum_bp_30
longt5_xl_summ_screen_memsum_bp_30 is a machine learning model from learn3r. 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.
should probably proofread and complete it, then remove this comment. --
All-time downloads
51
Public
Repo size
11.4 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin11.4 GB · 100%
At a glance
- Library
- transformers
- Model type
- longt5
- Access
- Public
- Created
- Sep 19, 2023
- Updated
- Sep 19, 2023
- SHA
- c06f4cb8
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. -->
longt5_xl_summ_screen_memsum_bp_30
This model is a fine-tuned version of longt5_xl_summ_screen_memsum_bp_20/checkpoint-140 on the learn3r/summ_screen_fd_memsum_bp dataset. It achieves the following results on the evaluation set:
- Loss: 2.6817
- Rouge1: 47.1842
- Rouge2: 18.22
- Rougel: 28.4626
- Rougelsum: 45.5778
- Gen Len: 308.9083
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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0707 | 0.97 | 14 | 2.7097 | 41.4751 | 15.5831 | 25.1976 | 39.9229 | 453.5296 |
| 0.0608 | 1.95 | 28 | 2.7271 | 45.691 | 17.905 | 27.9519 | 43.8787 | 387.4172 |
| 0.0851 | 2.99 | 43 | 3.0001 | 47.1647 | 17.8993 | 28.7561 | 45.661 | 261.5680 |
| 0.0697 | 3.97 | 57 | 2.9297 | 46.6892 | 17.8922 | 28.0724 | 44.8821 | 365.3047 |
| 0.0296 | 4.94 | 71 | 2.9017 | 44.2702 | 17.7874 | 26.7598 | 42.6857 | 440.6391 |
| 0.0312 | 5.98 | 86 | 3.0489 | 47.7884 | 18.1788 | 28.6688 | 46.0744 | 306.6716 |
| 0.0383 | 6.96 | 100 | 2.6817 | 47.1842 | 18.22 | 28.4626 | 45.5778 | 308.9083 |
| 0.0367 | 8.0 | 115 | 3.0245 | 45.5573 | 17.2161 | 28.0573 | 43.7772 | 227.8550 |
| 0.04 | 8.97 | 129 | 3.2873 | 44.0164 | 17.1682 | 26.4769 | 42.3752 | 429.8757 |
| 0.028 | 9.74 | 140 | 2.9815 | 46.6542 | 17.8515 | 28.146 | 45.0274 | 337.4822 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Library
- transformers
- Type
- longt5
- Created
- Sep 19, 2023
- Updated
- Sep 19, 2023