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unlearn_samsum_t5-base_scrub_4_42
jialicheng/unlearn_samsum_t5-base_scrub_4_42
unlearn_samsum_t5-base_scrub_4_42 is a machine learning model from jialicheng. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Nov 8, 2025
- Updated
- Nov 8, 2025
- SHA
- 53728c0f
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. -->
samsum_42
This model is a fine-tuned version of google/t5-v1_1-base on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 75.9089
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 127.0
- Test Rougel: 0.0
- Df Rougel: 0.0
- Unlearn Overall Rougel: 0.5
- Unlearn Time: 1550.0700
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Overall Rougel | Unlearn Overall Rougel | Time |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 441 | 75.9089 | 0.0 | 0.0 | 0.0 | 0.0 | 127.0 | 0.5 | 0.5 | -1 |
| No log | 2.0 | 882 | 78.7455 | 0.0 | 0.0 | 0.0 | 0.0 | 127.0 | 0.5 | 0.5 | -1 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
- Type
- t5
- License
- apache-2.0
- Created
- Nov 8, 2025
- Updated
- Nov 8, 2025