Sentiment Analysis Fine Tuner

Bases: OpenAIFineTuner

A bolt for fine-tuning OpenAI models for translation tasks.

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

Parameters:

Name	Type	Description	Default
input	BatchInput	The batch input data.	required
output	BatchOutput	The output data.	required
state	State	The state manager.	required

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, origin='en', target='fr', **kwargs)

Load a dataset from a directory.

Supported Data Formats and Structures for Translation Tasks:

JSONL

Each line is a JSON object representing an example.

{
    "translation": {
        "en": "English text",
        "fr": "French text"
    }
}

CSV

Should contain 'en' and 'fr' columns.

en,fr
"English text","French text"

Parquet

Should contain 'en' and 'fr' columns.

JSON

An array of dictionaries with 'en' and 'fr' keys.

[
    {
        "en": "English text",
        "fr": "French text"
    }
]

XML

Each 'record' element should contain 'en' and 'fr' child elements.

<record>
    <en>English text</en>
    <fr>French text</fr>
</record>

YAML

Each document should be a dictionary with 'en' and 'fr' keys.

- en: "English text"
  fr: "French text"

TSV

Should contain 'en' and 'fr' columns separated by tabs.

Excel (.xls, .xlsx)

Should contain 'en' and 'fr' columns.

SQLite (.db)

Should contain a table with 'en' and 'fr' columns.

Feather

Should contain 'en' and 'fr' columns.

Parameters:

Name	Type	Description	Default
dataset_path	str	The path to the directory containing the dataset files.	required
max_length	int	The maximum length for tokenization. Defaults to 512.	required
origin	str	The origin language. Defaults to 'en'.	'en'
target	str	The target language. Defaults to 'fr'.	'fr'
**kwargs		Additional keyword arguments.	{}

Returns:

Name	Type	Description
DatasetDict	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	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'.