Natural Language Inference Fine Tuner

Bases: OpenAIFineTuner

A bolt for fine-tuning OpenAI models for commonsense reasoning tasks.

This bolt uses the OpenAI API to fine-tune a pre-trained model for commonsense reasoning.

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 commonsense reasoning dataset from a directory.

Parameters:

Name	Type	Description	Default
dataset_path	str	The path to the dataset directory.	required
**kwargs	Any	Additional keyword arguments.	{}

Returns:

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

Raises:

Type	Description
Exception	If there was an error loading the dataset.

Supported Data Formats and Structures:

Hugging Face Dataset

Dataset files saved by the Hugging Face datasets library.

JSONL

Each line is a JSON object representing an example.

CSV

Should contain 'premise', 'hypothesis', and 'label' columns.

Parquet

Should contain 'premise', 'hypothesis', and 'label' columns.

JSON

An array of dictionaries with 'premise', 'hypothesis', and 'label' keys.

XML

Each 'record' element should contain 'premise', 'hypothesis', and 'label' child elements.

YAML

Each document should be a dictionary with 'premise', 'hypothesis', and 'label' keys.

TSV

Should contain 'premise', 'hypothesis', and 'label' columns separated by tabs.

Excel (.xls, .xlsx)

Should contain 'premise', 'hypothesis', and 'label' columns.

SQLite (.db)

Should contain a table with 'premise', 'hypothesis', and 'label' columns.

Feather

Should contain 'premise', 'hypothesis', and 'label' columns.

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'.