metadata

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

gpt2-jokes

This model is a fine-tuned version of 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