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t5-base-finetuned-context-dataset
mzhou08/t5-base-finetuned-context-dataset
t5-base-finetuned-context-dataset is a machine learning model from mzhou08. 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. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Oct 25, 2022
- Updated
- Oct 25, 2022
- SHA
- 16c31fad
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. -->
t5-base-finetuned-context-dataset
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9585
- Rouge1: 69.1959
- Rouge2: 19.8377
- Rougel: 68.8143
- Rougelsum: 68.5662
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 60 | 1.0390 | 69.1959 | 19.8377 | 68.8143 | 68.5662 |
| No log | 2.0 | 120 | 0.9856 | 69.1959 | 19.8377 | 68.8143 | 68.5662 |
| No log | 3.0 | 180 | 0.9585 | 69.1959 | 19.8377 | 68.8143 | 68.5662 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Oct 25, 2022
- Updated
- Oct 25, 2022