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This validator will only print out the number of examples and recommend to the user to increase the number of examples if less than 100.
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<listcomp>- s zInecessary_column_validator.<locals>.lower_case_column.<locals>.<listcomp>r T)�columns�inplace)r0 �renamer* )r r( �colsr r. r �lower_case_column, s z5necessary_column_validator.<locals>.lower_case_columnNc S s g | ]}t |��� �qS r r) r+ r r r r/ 7 � z.necessary_column_validator.<locals>.<listcomp>c �
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This validator will remove additional columns from the dataframe.
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WARNING: Some of the additional columns/keys contain `z<` in their name. These will be ignored, and the column/key `z`` will be used instead. This could also result from a duplicate column/key in the provided file.zh
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This validator will ensure that no completion is empty.
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This validator will suggest to the user to remove duplicate rows if they exist.
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This validator will suggest to the user to remove examples that are too long.
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long_examplesr r r �get_long_indexes� s z1long_examples_validator.<locals>.get_long_indexesr r^ z. examples that are very long. These are rows: zf
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This validator will suggest to add a common suffix to the prompt if one doesn't already exist in case of classification or conditional generation.
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WARNING: Some of your prompts contain the suffix `zZ` more than once. We strongly suggest that you review your prompts and add a unique suffixa�
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This validator will suggest to remove a common prefix from the prompt if a long one exist.
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- All prompts start with prefix `r; z�. Fine-tuning doesn't require the instruction specifying the task, or a few-shot example scenario. Most of the time you should only add the input data into the prompt, and the desired output into the completion�Remove prefix `z` from all promptsc s
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This validator will suggest to remove a common prefix from the completion if a long one exist.
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- All completions start with prefix `z_`. Most of the time you should only add the output data into the completion, without any prefixr� z` from all completionsc s �| � ��S r7 r rD �r� r� r� r r r E ra z7common_completion_prefix_validator.<locals>.optional_fn�common_completion_prefixrc N)rC r r� r r� r r r rG )r� r> r# r r� r� r r� r �"common_completion_prefix_validator, s"
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This validator will suggest to add a common suffix to the completion if one doesn't already exist in case of classification or conditional generation.
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Ensure completions are different, otherwise the model will just repeat `r; r� z [END]) rn �.z ENDz***z+++z&&&z$$$z@@@z%%%Frz rn r| rC r r c S r� �Nr> r r� r r r r� v r� z6common_completion_suffix_validator.<locals>.add_suffixr z$
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WARNING: Some of your completions contain the suffix `zU` more than once. We suggest that you review your completions and add a unique endingaH
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This validator will suggest to add a space at the start of the completion if it doesn't already exist. This helps with tokenization.
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startswith)rS r r r rM � � zLcompletions_space_start_validator.<locals>.add_space_start.<locals>.<lambda>)rU rD r r r �add_space_start� s z:completions_space_start_validator.<locals>.add_space_startNrj r r� z�
- The completion should start with a whitespace character (` `). This tends to produce better results due to the tokenization we use. See https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detailsz=Add a whitespace character to the beginning of the completion�completion_space_startrc rG )r> r �nunique�valuesr )r r� r r r r r r �!completions_space_start_validator� s
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This validator will suggest to lowercase the column values, if more than a third of letters are uppercase.
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lower_case� s z(lower_case_validator.<locals>.lower_casec S � t dd� | D ��S )Nc s �$ � | ]
}|� � r|�� rd V qdS �rj N)�isalpha�isupperr+ r r r � <genexpr>� � �" �9lower_case_validator.<locals>.<lambda>.<locals>.<genexpr>��sumrD r r r rM � � z&lower_case_validator.<locals>.<lambda>c S r� )Nc s r� r� )r� �islowerr+ r r r r� � r� r� r� rD r r r rM � r� r r� z
- More than a third of your `z%` column/key is uppercase. Uppercase z�s tends to perform worse than a mixture of case encountered in normal language. We recommend to lower case the data if that makes sense in your domain. See https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detailsz'Lowercase all your data in column/key `r; rc NrG )rU r� r )r r( r� � count_upper� count_lowerr r. r �lower_case_validator� s
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tj| td��d �}Y n!w d!}d"| v �r&|d#| � d$| �d"�d% � d&�7 }n|d#| � d'�7 }W n' ttf�yP | �d"�d% �� }d(| � d)|� d*|� d+�}Y nw d,| � d-�}td.|||d/�}||fS )0z�
This function will read a file saved in .csv, .json, .txt, .xlsx or .tsv format using pandas.
- for .xlsx it will read the first sheet
- for .txt it will assume completions and split on newline
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- Based on your file extension, your file is formatted as a z filez
Your format `z ` will be converted to `JSONL`)�sep�dtyper z.xlsxzH
- Based on your file extension, your file is formatted as an Excel filez/Your format `XLSX` will be converted to `JSONL`rj z�
- Your Excel file contains more than one sheet. Please either save as csv or ensure all data is present in the first sheet. WARNING: Reading only the first sheet...)r� z.txtz9
- Based on your file extension, you provided a text filez.Your format `TXT` will be converted to `JSONL`�rc S s g | ]}d |g�qS )r r )r, �liner r r r/ � s z#read_any_format.<locals>.<listcomp>rn )r0 r� �.jsonlT)�linesr� z^
- Your JSONL file appears to be in a JSON format. Your file will be converted to JSONL formatz/Your format `JSON` will be converted to `JSONL`z.jsonz^
- Your JSON file appears to be in a JSONL format. Your file will be converted to JSONL formatz]Your file must have one of the following extensions: .CSV, .TSV, .XLSX, .TXT, .JSON or .JSONLr� z Your file `z ` ends with the extension `.���z` which is not supported.z ` is missing a file extension.z Your file `z!` does not appear to be in valid z9 format. Please ensure your file is formatted as a valid z file.zFile z does not exist.�read_any_format)r
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This validator will infer the likely fine-tuning format of the data, and display it to the user if it is classification.
It will also suggest to use ada and explain train/validation split benefits.
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- For classification, we recommend you try one of the faster and cheaper models, such as `ada`
- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used for trainingr! r" )rv r )r rw r r r r �format_inferrer_validator s
r� r� c C sb |j durtj�d|j� d|j � d�� t�d� |jdur%tj�|j� |jdur/|�| �} | S )zs
This function will apply a necessary remediation to a dataframe, or print an error message if one exists.
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This function will apply an optional remediation to a dataframe, based on the user input.
Fz- [Recommended] z [Y/n]: NTz- [Necessary] rn )r r� r r ro rp rq )r r� r� �optional_appliedr� r r r �apply_optional_remediation> s
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Estimate the time it'll take to fine-tune the dataset
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�?T)rY g��|?5^�?�time�floatr r c S sd | dk r t | d�� d�S | dk rt | d d�� d�S | dk r(t | d d�� d�S t | d d�� d�S ) N�< r z secondsi z minutesi�Q z hoursz days)�round)r� r r r � format_time] s z.estimate_fine_tuning_time.<locals>.format_time� z:Once your model starts training, it'll approximately take z~ to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.
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This function will write out a dataframe to a file, if the user would like to proceed, and also offer a fine-tuning command with the newly created file.
For classification it will optionally ask the user if they would like to split the data into train/valid files, and modify the suggested command to include the valid set.
r r� FzQ- [Recommended] Would you like to split into training and validation set? [Y/n]: r� Tr rn r| r z Make sure to include `stop=["z;"]` so that the generated texts ends at the expected place.z@
Your data will be written to a new JSONL file. Proceed [Y/n]: zK
You can use your file for fine-tuning:
> openai api fine_tunes.create -t "�"ue
After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `zX` for the model to start generating completions, rather than continuing with the prompt.r �train�validrj i� g�������?�* )r� � random_stater= r> �recordsN)r� �orient� force_ascii�indentz! --compute_classification_metricsz" --classification_positive_class "z --classification_n_classes rS z to `z` and `z -v "uc After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `z
Wrote modified filezd`
Feel free to take a look!
Now use that file when fine-tuning:
> openai api fine_tunes.create -t "z
z#Aborting... did not write the file
)rv r� r= r> r� r� r# ro rp rq r r �max�int�samplerr rY �to_jsonr re )r r� r r� r �common_prompt_suffixr� r� r� �additional_params�%common_prompt_suffix_new_line_handled�)common_completion_suffix_new_line_handled�optional_ending_string�fnames�MAX_VALID_EXAMPLES�n_train�df_train�df_validr r � files_string� valid_string�separator_reminderr r r �write_out_file� sn
�� �
( � � ��
�(� r1 c C s> d}t | jj�� �dkrdS t| j�� �t| �| k r dS dS )z>
Infer the likely fine-tuning task type from the data
� r rh r� zconditional generation)r� r= r r# r> �unique)r �CLASSIFICATION_THRESHOLDr r r rv � s rv r �seriesr� c C sn d} |dkr| j t|�d d� n
| j dt|�d � }|�� dkr' |S ||jd kr1 |S |jd }q)zQ
Finds the longest common suffix or prefix of all the values in a series
r Tr rj Nr )r r# r� r� )r5 r� � common_xfix�
common_xfixesr r r r� � s 4� �
��r� z,Callable[[pd.DataFrame], Remediation | None]r � Validator�list[Validator]c C s2 t dd� dd� tttttdd� dd� tttt t
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z get_validators.<locals>.<lambda>c S r: r� r; rD r r r rM � r< c S r: r� �r� rD r r r rM � r< c S r: r� r= rD r r r rM � r< ) r& rK r] r� rg rx r� r� r� r� r� r r r r �get_validators� s �r> �
validators�write_out_file_func�Callable[..., Any]c
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Based on the analysis we will perform the following actions:
z
No remediations found.
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r r� r� r? r� r@ �optional_remediations� validator�&any_optional_or_necessary_remediations�any_necessary_applied�any_optional_appliedr� �!any_optional_or_necessary_appliedr r r �apply_validators s6
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r r r� r r r� r� r� r r� )r r r r )r )r5 r r� r r r )r r9 )r r r� r r� r� r? r9 r� r� r@ rA r r� ),�
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