GPT3DataGen is a python package that generates fake data for fine-tuning your openai models.
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`\__ || ,__/'`\__)`\____)`\__,_)`\__,_)`\__)`\__,_)`\__ |`\____)(_) (_)v0.1.0
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Install with pip. See Install & Usage Guide
pip install -U gpt3datagenAlternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:
pip install git+https://github.com/donwany/gpt3datagen.git --use-pep517Or git clone repository:
git clone https://github.com/donwany/gpt3datagen.git
cd gpt3datagen
make install && pip install -e .To update the package to the latest version of this repository, please run:
pip install --upgrade --no-deps --force-reinstall git+https://github.com/donwany/gpt3datagen.gitRun the following to view all available options:
gpt3datagen --help
gpt3datagen --versionOutput formats: jsonl, json, csv, tsv, xlsx
gpt3datagen \
--num_samples 500 \
--max_length 2048 \
--sample_type "classification" \
--output_format "jsonl" \
--output_dir .
gpt3datagen \
--num_samples 500 \
--max_length 2048 \
--sample_type completion \
--output_format csv \
--output_dir .
gpt3datagen \
--sample_type completion \
--output_format jsonl \
--output_dir .
gpt3datagen --sample_type completion -o . -f jsonl
gpt3datagen --sample_type news -o . -f jsonl{"prompt": "<prompt text> \n\n###\n\n", "completion": " <ideal generated text> END"}
{"prompt": "<prompt text> \n\n###\n\n", "completion": " <ideal generated text> END"}
{"prompt": "<prompt text> \n\n###\n\n", "completion": " <ideal generated text> END"}
...Only useful if you clone the repository
python prepare.py \
--num_samples 500 \
--max_length 2048 \
--sample_type "classification" \
--output_format "jsonl" \
--output_dir .
python prepare.py \
--num_samples 500 \
--max_length 2048 \
--sample_type "completion" \
--output_format "csv" \
--output_dir .
python prepare.py \
--num_samples 500 \
--max_length 2048 \
--sample_type "completion" \
--output_format "json" \
--output_dir /Users/<tsiameh>/Desktoppip install --upgrade openai
export OPENAI_API_KEY="<OPENAI_API_KEY>"
# validate sample datasets generated
openai tools fine_tunes.prepare_data -f <SAMPLE_DATA>.jsonl
openai tools fine_tunes.prepare_data -f <SAMPLE_DATA>.csv
openai tools fine_tunes.prepare_data -f <SAMPLE_DATA>.tsv
openai tools fine_tunes.prepare_data -f <SAMPLE_DATA>.json
openai tools fine_tunes.prepare_data -f <SAMPLE_DATA>.xlsx
openai tools fine_tunes.prepare_data -f /Users/<tsiameh>/Desktop/data_prepared.jsonl
# fine-tune
openai api fine_tunes.create \
-t <DATA_PREPARED>.jsonl \
-m <BASE_MODEL: davinci, curie, ada, babbage>
# List all created fine-tunes
openai api fine_tunes.list# For multiclass classification
openai api fine_tunes.create \
-t <TRAIN_FILE_ID_OR_PATH> \
-v <VALIDATION_FILE_OR_PATH> \
-m <MODEL> \
--compute_classification_metrics \
--classification_n_classes <N_CLASSES>
# For binary classification
openai api fine_tunes.create \
-t <TRAIN_FILE_ID_OR_PATH> \
-v <VALIDATION_FILE_OR_PATH> \
-m <MODEL> \
--compute_classification_metrics \
--classification_n_classes 2 \
--classification_positive_class <POSITIVE_CLASS_FROM_DATASET>Please see CONTRIBUTING.
GPT3DataGen is released under the MIT License. See the bundled LICENSE file for details.
Theophilus Siameh
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