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# python-bytes-distilgpt2

This model is not affiliated with the Python Bytes podcast in any way.

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [Python Bytes show notes](https://github.com/mikeckennedy/python_bytes_show_notes/tree/master/transcripts).

It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969

## Model description

This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :).

## Intended uses & limitations

This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner.

## Training and evaluation data

Data is located [on GitHub](https://github.com/mikeckennedy/python_bytes_show_notes/tree/master/transcripts)

## 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 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "fastAPI is a great new web framework to easily build web APIs.", "example_title": "fastAPI"}, {"text": "The new tool uses sqlite for performance", "example_title": "sqlite"}], "model-index": [{"name": "python-bytes-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 




# python-bytes-distilgpt2

This model is not affiliated with the Python Bytes podcast in any way.

This model is a fine-tuned version of distilgpt2 on Python Bytes show notes.

It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969

## Model description

This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :).

## Intended uses & limitations

This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner.

## Training and evaluation data

Data is located on GitHub

## 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 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# python-bytes-distilgpt2

This model is not affiliated with the Python Bytes podcast in any way.

This model is a fine-tuned version of distilgpt2 on Python Bytes show notes.

It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969 ## Model description

This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :). ## Intended uses & limitations

This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner. ## Training and evaluation data

Data is located on GitHub ## 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 1.12.0+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 
 # python-bytes-distilgpt2

This model is not affiliated with the Python Bytes podcast in any way.

This model is a fine-tuned version of distilgpt2 on Python Bytes show notes.

It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969 ## Model description

This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :). ## Intended uses & limitations

This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner. ## Training and evaluation data

Data is located on GitHub ## 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 1.12.0+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 
# python-bytes-distilgpt2

This model is not affiliated with the Python Bytes podcast in any way.

This model is a fine-tuned version of distilgpt2 on Python Bytes show notes.

It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969## Model description

This model generates conversation between the two show hosts (Michael Kennedy and Brian Okken), and sometimes guests appear :).## Intended uses & limitations

This model was trained specifically for educational purposes and is intended for other users to use it in a similar manner.## Training and evaluation data

Data is located on GitHub## 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 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1