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flan-t5-base
Mrf01/flan-t5-base
flan-t5-base is a machine learning model from Mrf01. 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
- May 20, 2023
- Updated
- Jun 3, 2023
- SHA
- f256f0c5
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-base
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0724
- Rouge1 Precision: 0.0
- Rouge1 Recall: 0.0
- Rouge1 Fmeasure: 0.0
- Rouge2 Precision: 0.0
- Rouge2 Recall: 0.0
- Rouge2 Fmeasure: 0.0
- Rougel Precision: 0.0
- Rougel Recall: 0.0
- Rougel Fmeasure: 0.0
- Rougelsum Precision: 0.0
- Rougelsum Recall: 0.0
- Rougelsum Fmeasure: 0.0
- Bleu-1: 0.0
- Bleu-2: 0.0
- Bleu-3: 0.0
- Bleu-4: 0.0
- Meteor: 0.0006
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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0641 | 1.0 | 10830 | 0.0726 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0613 | 2.0 | 21660 | 0.0759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0003 |
| 0.0628 | 3.0 | 32490 | 0.0755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 |
| 0.0631 | 4.0 | 43320 | 0.0724 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0006 |
| 0.0597 | 5.0 | 54150 | 0.0741 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0006 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- t5
- Created
- May 20, 2023
- Updated
- Jun 3, 2023