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---
license: mit
tags:
- generated_from_trainer
datasets:
- Fraser/short-jokes
metrics:
- accuracy
model-index:
- name: gpt2-jokes
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: Fraser/short-jokes
type: Fraser/short-jokes
config: default
split: train[:5%]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8795507387461411
---
<!-- 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. -->
# gpt2-jokes
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the Fraser/short-jokes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6748
- Accuracy: 0.8796
## 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: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.06 | 100 | 0.7285 | 0.8732 |
| No log | 0.12 | 200 | 0.7141 | 0.8747 |
| No log | 0.17 | 300 | 0.7056 | 0.8757 |
| No log | 0.23 | 400 | 0.6992 | 0.8764 |
| 0.7907 | 0.29 | 500 | 0.6942 | 0.8771 |
| 0.7907 | 0.35 | 600 | 0.6906 | 0.8777 |
| 0.7907 | 0.41 | 700 | 0.6873 | 0.8779 |
| 0.7907 | 0.47 | 800 | 0.6848 | 0.8782 |
| 0.7907 | 0.52 | 900 | 0.6830 | 0.8786 |
| 0.7105 | 0.58 | 1000 | 0.6809 | 0.8788 |
| 0.7105 | 0.64 | 1100 | 0.6794 | 0.8790 |
| 0.7105 | 0.7 | 1200 | 0.6780 | 0.8792 |
| 0.7105 | 0.76 | 1300 | 0.6770 | 0.8793 |
| 0.7105 | 0.81 | 1400 | 0.6760 | 0.8794 |
| 0.7034 | 0.87 | 1500 | 0.6755 | 0.8794 |
| 0.7034 | 0.93 | 1600 | 0.6750 | 0.8795 |
| 0.7034 | 0.99 | 1700 | 0.6748 | 0.8795 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0-rc1
- Datasets 2.10.1
- Tokenizers 0.13.2
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