Question Answering Fine Tuner

Bases: OpenAIFineTuner

A bolt for fine-tuning OpenAI models on question answering tasks.

CLI Usage:

    genius HuggingFaceCommonsenseReasoningFineTuner rise \
        batch \
            --input_s3_bucket geniusrise-test \
            --input_s3_folder train \
        batch \
            --output_s3_bucket geniusrise-test \
            --output_s3_folder model \
        fine_tune \
            --args model_name=my_model tokenizer_name=my_tokenizer num_train_epochs=3 per_device_train_batch_size=8

YAML Configuration:

    version: "1"
    bolts:
        my_fine_tuner:
            name: "HuggingFaceCommonsenseReasoningFineTuner"
            method: "fine_tune"
            args:
                model_name: "my_model"
                tokenizer_name: "my_tokenizer"
                num_train_epochs: 3
                per_device_train_batch_size: 8
                data_max_length: 512
            input:
                type: "batch"
                args:
                    bucket: "my_bucket"
                    folder: "my_dataset"
            output:
                type: "batch"
                args:
                    bucket: "my_bucket"
                    folder: "my_model"
            deploy:
                type: k8s
                args:
                    kind: deployment
                    name: my_fine_tuner
                    context_name: arn:aws:eks:us-east-1:genius-dev:cluster/geniusrise-dev
                    namespace: geniusrise
                    image: geniusrise/geniusrise
                    kube_config_path: ~/.kube/config

Supported Data Formats

• JSONL
• CSV
• Parquet
• JSON
• XML
• YAML
• TSV
• Excel (.xls, .xlsx)
• SQLite (.db)
• Feather

load_dataset(dataset_path, **kwargs)

Load a dataset from a directory.

Supported Data Formats and Structures:

JSONL

Each line is a JSON object representing an example.

{"context": "The context content", "question": "The question", "answers": {"answer_start": [int], "context": [str]}}

CSV

Should contain 'context', 'question', and 'answers' columns.

context,question,answers
"The context content","The question","{'answer_start': [int], 'text': [str]}"

Parquet

Should contain 'context', 'question', and 'answers' columns.

JSON

An array of dictionaries with 'context', 'question', and 'answers' keys.

[{"context": "The context content", "question": "The question", "answers": {"answer_start": [int], "context": [str]}}]

XML

Each 'record' element should contain 'context', 'question', and 'answers' child elements.

<record>
    <context>The context content</context>
    <question>The question</question>
    <answers answer_start="int" context="str"></answers>
</record>

YAML

Each document should be a dictionary with 'context', 'question', and 'answers' keys.

- context: "The context content"
  question: "The question"
  answers:
    answer_start: [int]
    context: [str]

TSV

Should contain 'context', 'question', and 'answers' columns separated by tabs.

Excel (.xls, .xlsx)

Should contain 'context', 'question', and 'answers' columns.

SQLite (.db)

Should contain a table with 'context', 'question', and 'answers' columns.

Feather

Should contain 'context', 'question', and 'answers' columns.

Parameters:

Name	Type	Description	Default
dataset_path	str	The path to the dataset directory.	required
pad_on_right	bool	Whether to pad on the right.	required
max_length	int	The maximum length of the sequences.	required
doc_stride	int	The document stride.	required
evaluate_squadv2	bool	Whether to evaluate using SQuAD v2 metrics.	required

Returns:

Name	Type	Description
Dataset	Union[Dataset, DatasetDict, Optional[Dataset]]	The loaded dataset.

prepare_fine_tuning_data(data, data_type)

Prepare the given data for fine-tuning.

Parameters:

Name	Type	Description	Default
data	Union[Dataset, DatasetDict, Optional[Dataset]]	The dataset to prepare.	required
data_type	str	Either 'train' or 'eval' to specify the type of data.	required

Raises:

Type	Description
ValueError	If data_type is not 'train' or 'eval'.