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# nba_pbp_distilgpt2

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.

It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117

## Model description

This model will generate properly formatted play-by-play descriptions of an NBA game with players from the Boston Celtics and Golden State Warriors.

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "1 11:45 0-0 K. Looney misses 2-pt layup from 4 ft (block by R. Williams)", "example_title": "GSW-BOS"}], "model-index": [{"name": "nba_pbp_distilgpt2", "results": []}]}
transformers pytorch tensorboard gpt2 text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# nba_pbp_distilgpt2

This model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.

It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117

## Model description

This model will generate properly formatted play-by-play descriptions of an NBA game with players from the Boston Celtics and Golden State Warriors.

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# nba_pbp_distilgpt2

This model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.

It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117 ## Model description

This model will generate properly formatted play-by-play descriptions of an NBA game with players from the Boston Celtics and Golden State Warriors. ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # nba_pbp_distilgpt2

This model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.

It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117 ## Model description

This model will generate properly formatted play-by-play descriptions of an NBA game with players from the Boston Celtics and Golden State Warriors. ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# nba_pbp_distilgpt2

This model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.

It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117## Model description

This model will generate properly formatted play-by-play descriptions of an NBA game with players from the Boston Celtics and Golden State Warriors.## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Training results### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1