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# 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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["Fraser/short-jokes"], "metrics": ["accuracy"], "model-index": [{"name": "gpt2-jokes", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "Fraser/short-jokes", "type": "Fraser/short-jokes", "config": "default", "split": "train[:5%]", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8795507387461411, "name": "Accuracy"}]}]}]}
transformers pytorch gpt2 text-generation generated_from_trainer dataset:Fraser/short-jokes license:mit model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.10.1
* Tokenizers 0.13.2