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vit5-base-vietnews-summarization-standardized-number
ThuyNT03/vit5-base-vietnews-summarization-standardized-number
vit5-base-vietnews-summarization-standardized-number is a machine learning model from ThuyNT03. 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 mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- t5
- Access
- Public
- Created
- Oct 10, 2023
- Updated
- Oct 10, 2023
- SHA
- e45cad7c
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. -->
vit5-base-vietnews-summarization-standardized-number
This model is a fine-tuned version of VietAI/vit5-base-vietnews-summarization on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8266
- Rouge1: 80.3861
- Rouge2: 75.4112
- Rougel: 80.0172
- Rougelsum: 79.9721
- Gen Len: 7.7504
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: 8
- seed: 42
- 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 |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 297 | 0.6401 | 77.9012 | 72.733 | 77.2694 | 77.2675 | 8.9022 |
| 0.7223 | 2.0 | 594 | 0.5931 | 78.4299 | 73.3202 | 78.0444 | 78.0744 | 8.7437 |
| 0.7223 | 3.0 | 891 | 0.6182 | 79.3604 | 74.2323 | 78.907 | 78.9878 | 7.5497 |
| 0.3472 | 4.0 | 1188 | 0.6134 | 79.7284 | 74.8063 | 79.3057 | 79.3545 | 8.5245 |
| 0.3472 | 5.0 | 1485 | 0.6625 | 79.7185 | 74.5798 | 79.3356 | 79.3615 | 8.2749 |
| 0.2388 | 6.0 | 1782 | 0.7068 | 79.7276 | 74.6552 | 79.2519 | 79.2253 | 8.0489 |
| 0.1551 | 7.0 | 2079 | 0.7646 | 80.3805 | 75.394 | 79.9394 | 79.8915 | 7.9983 |
| 0.1551 | 8.0 | 2376 | 0.7736 | 81.2428 | 76.358 | 80.8152 | 80.8272 | 7.6121 |
| 0.1127 | 9.0 | 2673 | 0.8175 | 80.8907 | 76.0278 | 80.3848 | 80.3953 | 7.6543 |
| 0.1127 | 10.0 | 2970 | 0.8266 | 80.3861 | 75.4112 | 80.0172 | 79.9721 | 7.7504 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- License
- mit
- Created
- Oct 10, 2023
- Updated
- Oct 10, 2023