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flan-t5-large-extraction-cnndm_4000-all
Zekunli/flan-t5-large-extraction-cnndm_4000-all
flan-t5-large-extraction-cnndm_4000-all 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Feb 5, 2023
- Updated
- Mar 4, 2023
- SHA
- fce13483
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_4000-all
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8084
- Rouge1: 35.2389
- Rouge2: 15.2731
- Rougel: 29.9899
- Rougelsum: 30.0262
- Gen Len: 19.0
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 |
|---|---|---|---|---|---|---|---|---|
| 1.2214 | 0.4 | 200 | 1.9330 | 34.7186 | 15.2527 | 29.7852 | 29.8623 | 19.0 |
| 1.2119 | 0.8 | 400 | 1.9119 | 34.718 | 15.3471 | 29.4347 | 29.4709 | 19.0 |
| 1.1482 | 1.2 | 600 | 2.0060 | 34.1536 | 15.0233 | 29.503 | 29.518 | 18.99 |
| 1.1102 | 1.6 | 800 | 2.0276 | 34.8004 | 15.1277 | 29.5782 | 29.6371 | 18.998 |
| 1.1295 | 2.0 | 1000 | 1.9375 | 35.1942 | 15.2087 | 30.156 | 30.0925 | 18.996 |
| 1.2045 | 2.4 | 1200 | 1.9016 | 35.5121 | 15.8033 | 30.515 | 30.5451 | 18.984 |
| 1.492 | 2.8 | 1400 | 1.8119 | 35.0575 | 15.2373 | 29.8621 | 29.9106 | 19.0 |
| 1.4535 | 3.2 | 1600 | 1.8160 | 35.0796 | 15.6135 | 30.1449 | 30.189 | 19.0 |
| 1.4087 | 3.6 | 1800 | 1.8223 | 34.9121 | 15.3203 | 29.7578 | 29.8006 | 18.998 |
| 1.4098 | 4.0 | 2000 | 1.8084 | 35.2389 | 15.2731 | 29.9899 | 30.0262 | 19.0 |
| 1.3759 | 4.4 | 2200 | 1.8357 | 35.4492 | 15.8883 | 30.1135 | 30.151 | 19.0 |
| 1.3565 | 4.8 | 2400 | 1.8347 | 34.6559 | 15.2567 | 29.5659 | 29.5704 | 19.0 |
| 1.3268 | 5.2 | 2600 | 1.8416 | 35.326 | 15.5918 | 29.841 | 29.8391 | 19.0 |
| 1.3204 | 5.6 | 2800 | 1.8445 | 35.4671 | 15.5422 | 30.169 | 30.1985 | 19.0 |
| 1.3271 | 6.0 | 3000 | 1.8374 | 35.4057 | 15.6566 | 30.2378 | 30.2328 | 18.998 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- t5
- Created
- Feb 5, 2023
- Updated
- Mar 4, 2023