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---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
library_name: peft
model-index:
- name: p-tuning-t2t-gpt2-large-with-sst2
results: []
---
<!-- 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-t2t-gpt2-large-with-sst2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4929
- Accuracy: 0.7741
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6984 | 1.0 | 4210 | 0.7020 | 0.5138 |
| 0.6766 | 2.0 | 8420 | 0.6971 | 0.5138 |
| 0.5377 | 3.0 | 12630 | 0.5625 | 0.7087 |
| 0.5375 | 4.0 | 16840 | 0.5481 | 0.7282 |
| 0.5209 | 5.0 | 21050 | 0.5278 | 0.7420 |
| 0.5235 | 6.0 | 25260 | 0.5357 | 0.7374 |
| 0.5311 | 7.0 | 29470 | 0.5342 | 0.7294 |
| 0.5545 | 8.0 | 33680 | 0.5135 | 0.7511 |
| 0.5406 | 9.0 | 37890 | 0.5138 | 0.7397 |
| 0.523 | 10.0 | 42100 | 0.5192 | 0.7580 |
| 0.5248 | 11.0 | 46310 | 0.4997 | 0.7706 |
| 0.559 | 12.0 | 50520 | 0.5063 | 0.7649 |
| 0.582 | 13.0 | 54730 | 0.4958 | 0.7741 |
| 0.4796 | 14.0 | 58940 | 0.4981 | 0.7706 |
| 0.4173 | 15.0 | 63150 | 0.4911 | 0.7626 |
| 0.4449 | 16.0 | 67360 | 0.4932 | 0.7718 |
| 0.4697 | 17.0 | 71570 | 0.4917 | 0.7741 |
| 0.3779 | 18.0 | 75780 | 0.4931 | 0.7729 |
| 0.449 | 19.0 | 79990 | 0.4929 | 0.7729 |
| 0.4938 | 20.0 | 84200 | 0.4929 | 0.7741 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.5.0
- Datasets 3.0.1
- Tokenizers 0.19.1 |