---
license: mit
tags:
- generated_from_trainer
datasets:
- short-jokes
metrics:
- accuracy
model-index:
- name: gpt2-jokes
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: short-jokes
      type: short-jokes
      config: default
      split: train[:5%]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8795477617316698
---

<!-- 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 short-jokes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6748
- Accuracy: 0.8795

## 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