Sentiment Analysis Fine Tuner

Bases: OpenAIFineTuner

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

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

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, **kwargs)

Load a 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:

Type	Description
Optional[DatasetDict]	Dataset | DatasetDict: The loaded dataset.

Supported Data Formats and Structures:

JSONL

Each line is a JSON object representing an example.

{"text": "The text content", "summary": "The summary"}

CSV

Should contain 'text' and 'summary' columns.

text,summary
"The text content","The summary"

Parquet

Should contain 'text' and 'summary' columns.

JSON

An array of dictionaries with 'text' and 'summary' keys.

[{"text": "The text content", "summary": "The summary"}]

XML

Each 'record' element should contain 'text' and 'summary' child elements.

<record>
    <text>The text content</text>
    <summary>The summary</summary>
</record>

YAML

Each document should be a dictionary with 'text' and 'summary' keys.

- text: "The text content"
  summary: "The summary"

TSV

Should contain 'text' and 'summary' columns separated by tabs.

Excel (.xls, .xlsx)

Should contain 'text' and 'summary' columns.

SQLite (.db)

Should contain a table with 'text' and 'summary' columns.

Feather

Should contain 'text' and 'summary' columns.

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'.