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# pythia-1.4b-deduped-jokes

This model is a fine-tuned version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) on the Fraser/short-jokes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0699
- Accuracy: 0.9870

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 400

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.06  | 100  | 0.0729          | 0.9866   |
| No log        | 0.12  | 200  | 0.0716          | 0.9868   |
| No log        | 0.17  | 300  | 0.0705          | 0.9869   |
| No log        | 0.23  | 400  | 0.0699          | 0.9870   |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0-rc1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Fraser/short-jokes"], "metrics": ["accuracy"], "model-index": [{"name": "pythia-1.4b-deduped-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": 0.986989308918276, "name": "Accuracy"}]}]}]}
transformers pytorch gpt_neox text-generation generated_from_trainer dataset:Fraser/short-jokes license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

pythia-1.4b-deduped-jokes
=========================


This model is a fine-tuned version of EleutherAI/pythia-1.4b-deduped on the Fraser/short-jokes dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0699
* Accuracy: 0.9870


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: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 400


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 400 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #gpt_neox #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 400 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #gpt_neox #text-generation #generated_from_trainer #dataset-Fraser/short-jokes #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 400### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0-rc1
* Datasets 2.11.0
* Tokenizers 0.13.3