Downloads · 30 days
3
9% of all-time downloads
flan-t5-large-extraction-cnndm_2000-all-ep10
Zekunli/flan-t5-large-extraction-cnndm_2000-all-ep10
flan-t5-large-extraction-cnndm_2000-all-ep10 is a machine learning model from Zekunli. 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
32
Public
Repo size
28.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin3.1 GB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Mar 4, 2023
- Updated
- Mar 5, 2023
- SHA
- 164103ce
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-large-extraction-cnndm_2000-all-ep10
This model is a fine-tuned version of google/flan-t5-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7621
- Rouge1: 34.9279
- Rouge2: 15.2498
- Rougel: 29.9179
- Rougelsum: 29.9082
- Gen Len: 18.986
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: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.1649 | 0.8 | 200 | 1.8161 | 34.9419 | 14.942 | 29.7831 | 29.8328 | 19.0 |
| 1.9114 | 1.6 | 400 | 1.7713 | 34.825 | 14.6572 | 29.7393 | 29.7646 | 18.986 |
| 1.7997 | 2.4 | 600 | 1.7917 | 34.1619 | 14.8104 | 29.6399 | 29.6155 | 18.99 |
| 1.7477 | 3.2 | 800 | 1.7771 | 35.0584 | 15.3149 | 29.9349 | 29.9183 | 18.99 |
| 1.6821 | 4.0 | 1000 | 1.7621 | 34.9279 | 15.2498 | 29.9179 | 29.9082 | 18.986 |
| 1.6301 | 4.8 | 1200 | 1.7796 | 34.3508 | 14.8522 | 29.5811 | 29.5785 | 18.99 |
| 1.597 | 5.6 | 1400 | 1.7669 | 35.3844 | 15.7633 | 30.4042 | 30.4473 | 18.99 |
| 1.5543 | 6.4 | 1600 | 1.7857 | 34.4823 | 15.0787 | 29.7926 | 29.7856 | 18.99 |
| 1.5473 | 7.2 | 1800 | 1.7854 | 34.7347 | 15.1301 | 29.8061 | 29.8492 | 18.99 |
| 1.5017 | 8.0 | 2000 | 1.7920 | 35.0512 | 15.2671 | 30.2014 | 30.2702 | 18.99 |
| 1.4874 | 8.8 | 2200 | 1.7978 | 35.1954 | 15.518 | 30.2292 | 30.2755 | 18.99 |
| 1.4798 | 9.6 | 2400 | 1.7938 | 35.1686 | 15.5524 | 30.3497 | 30.3741 | 18.99 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 4, 2023
- Updated
- Mar 5, 2023