Downloads · 30 days
4
2% of all-time downloads
ft-t5-small-nl-2-fol-v1
tuanbc88/ft-t5-small-nl-2-fol-v1
ft-t5-small-nl-2-fol-v1 is a machine learning model from tuanbc88. 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. --
All-time downloads
252
Public
Parameters
60.5M
122 MB on disk
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors121 MB · 97%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Oct 27, 2024
- Updated
- Oct 27, 2024
- SHA
- 8107d7ae
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. -->
ft-t5-small-nl-2-fol-v1
This model is a fine-tuned version of t5-small on the yuan-yang/MALLS-v0, alevkov95/text2log dataset. It achieves the following results on the evaluation set:
- Loss: 1.0732
- Top-1 accuracy: 0.0
- Bleu Score: 0.3056
- Rouge1: 0.5254
- Rouge2: 0.2795
- Rougel: 0.5082
- Rougelsum: 0.5083
- Exact Match: 0.0
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Top-1 accuracy | Bleu Score | Rouge1 | Rouge2 | Rougel | Rougelsum | Exact Match |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.6921 | 1.0 | 3231 | 1.0767 | 0.0 | 0.3052 | 0.5249 | 0.2786 | 0.5076 | 0.5077 | 0.0 |
| 1.688 | 2.0 | 6462 | 1.0741 | 0.0 | 0.3056 | 0.5254 | 0.2795 | 0.5081 | 0.5082 | 0.0 |
| 1.679 | 3.0 | 9693 | 1.0734 | 0.0 | 0.3056 | 0.5254 | 0.2796 | 0.5081 | 0.5082 | 0.0 |
| 1.6846 | 4.0 | 12924 | 1.0733 | 0.0 | 0.3058 | 0.5255 | 0.2798 | 0.5083 | 0.5083 | 0.0 |
| 1.6889 | 5.0 | 16155 | 1.0734 | 0.0 | 0.3056 | 0.5253 | 0.2798 | 0.5082 | 0.5083 | 0.0 |
| 1.6725 | 6.0 | 19386 | 1.0733 | 0.0 | 0.3056 | 0.5254 | 0.2799 | 0.5084 | 0.5084 | 0.0 |
| 1.6771 | 7.0 | 22617 | 1.0733 | 0.0 | 0.3056 | 0.5254 | 0.2797 | 0.5083 | 0.5083 | 0.0 |
| 1.6843 | 8.0 | 25848 | 1.0734 | 0.0 | 0.3056 | 0.5255 | 0.2797 | 0.5084 | 0.5084 | 0.0 |
| 1.6651 | 9.0 | 29079 | 1.0733 | 0.0 | 0.3054 | 0.5252 | 0.2795 | 0.5081 | 0.5082 | 0.0 |
| 1.7005 | 10.0 | 32310 | 1.0732 | 0.0 | 0.3056 | 0.5254 | 0.2795 | 0.5082 | 0.5083 | 0.0 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Oct 27, 2024
- Updated
- Oct 27, 2024