Downloads · 30 days
2
8% of all-time downloads
p-tuning-gpt2-large-with-sst2
kisejin/p-tuning-gpt2-large-with-sst2
p-tuning-gpt2-large-with-sst2 is a machine learning model from kisejin. 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 peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
26
Public
Repo size
69.8 KB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.json2.9 MB · 85%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Oct 22, 2024
- Updated
- Oct 22, 2024
- SHA
- cfa163f7
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. -->
p-tuning-gpt2-large-with-sst2
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3065
- Accuracy: 0.8968
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4485 | 1.0 | 4210 | 0.3556 | 0.8406 |
| 0.2677 | 2.0 | 8420 | 0.3760 | 0.8693 |
| 0.2717 | 3.0 | 12630 | 0.3264 | 0.8727 |
| 0.3687 | 4.0 | 16840 | 0.3410 | 0.8807 |
| 0.3483 | 5.0 | 21050 | 0.3075 | 0.8865 |
| 0.2969 | 6.0 | 25260 | 0.3315 | 0.8888 |
| 0.3577 | 7.0 | 29470 | 0.2875 | 0.8853 |
| 0.4036 | 8.0 | 33680 | 0.3143 | 0.8899 |
| 0.404 | 9.0 | 37890 | 0.2858 | 0.8911 |
| 0.2797 | 10.0 | 42100 | 0.3035 | 0.8876 |
| 0.3328 | 11.0 | 46310 | 0.3168 | 0.8876 |
| 0.2345 | 12.0 | 50520 | 0.3063 | 0.8933 |
| 0.3154 | 13.0 | 54730 | 0.2972 | 0.8911 |
| 0.2937 | 14.0 | 58940 | 0.2994 | 0.8933 |
| 0.2123 | 15.0 | 63150 | 0.2938 | 0.8899 |
| 0.295 | 16.0 | 67360 | 0.3087 | 0.8945 |
| 0.1924 | 17.0 | 71570 | 0.3031 | 0.8933 |
| 0.2415 | 18.0 | 75780 | 0.3067 | 0.8979 |
| 0.1876 | 19.0 | 79990 | 0.3080 | 0.8979 |
| 0.3891 | 20.0 | 84200 | 0.3065 | 0.8968 |
Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.5.0
- Datasets 3.0.1
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- Oct 22, 2024
- Updated
- Oct 22, 2024