Classification

Bases: TextBulk

TextClassificationBulk is designed to handle bulk text classification tasks using Hugging Face models efficiently and effectively. It allows for processing large datasets, utilizing state-of-the-art machine learning models to provide accurate classification of text data into predefined labels.

Parameters:

Name	Type	Description	Default
input	BatchInput	Configuration and data inputs for the batch process.	required
output	BatchOutput	Configurations for output data handling.	required
state	State	State management for the classification task.	required
**kwargs		Arbitrary keyword arguments for extended configurations.	{}

Example CLI Usage:

genius TextClassificationBulk rise \
    batch \
        --input_folder ./input \
    batch \
        --output_folder ./output \
    none \
    --id cardiffnlp/twitter-roberta-base-hate-multiclass-latest-lol \
    classify \
        --args \
            model_name="cardiffnlp/twitter-roberta-base-hate-multiclass-latest" \
            model_class="AutoModelForSequenceClassification" \
            tokenizer_class="AutoTokenizer" \
            use_cuda=True \
            precision="bfloat16" \
            quantization=0 \
            device_map="auto" \
            max_memory=None \
            torchscript=False

__init__(input, output, state, **kwargs)

Initializes the TextClassificationBulk class with input, output, and state configurations.

Parameters:

Name	Type	Description	Default
input	BatchInput	Configuration for the input data.	required
output	BatchOutput	Configuration for the output data.	required
state	State	State management for the classification task.	required
**kwargs		Additional keyword arguments for extended functionality.	{}

classify(model_name, model_class='AutoModelForSequenceClassification', tokenizer_class='AutoTokenizer', use_cuda=False, precision='float', quantization=0, device_map='auto', max_memory={0: '24GB'}, torchscript=False, compile=False, awq_enabled=False, flash_attention=False, batch_size=32, notification_email=None, **kwargs)

Perform bulk classification using the specified model and tokenizer. This method handles the entire classification process including loading the model, processing input data, predicting classifications, and saving the results.

Parameters:

Name	Type	Description	Default
model_name	str	Name or path of the model.	required
model_class	str	Class name of the model (default "AutoModelForSequenceClassification").	'AutoModelForSequenceClassification'
tokenizer_class	str	Class name of the tokenizer (default "AutoTokenizer").	'AutoTokenizer'
use_cuda	bool	Whether to use CUDA for model inference (default False).	False
precision	str	Precision for model computation (default "float").	'float'
quantization	int	Level of quantization for optimizing model size and speed (default 0).	0
device_map	str | Dict | None	Specific device to use for computation (default "auto").	'auto'
max_memory	Dict	Maximum memory configuration for devices.	{0: '24GB'}
torchscript	bool	Whether to use a TorchScript-optimized version of the pre-trained language model. Defaults to False.	False
compile	bool	Whether to compile the model before fine-tuning. Defaults to True.	False
awq_enabled	bool	Whether to enable AWQ optimization (default False).	False
flash_attention	bool	Whether to use flash attention optimization (default False).	False
batch_size	int	Number of classifications to process simultaneously (default 32).	32
**kwargs	Any	Arbitrary keyword arguments for model and generation configurations.	{}

load_dataset(dataset_path, max_length=512, **kwargs)

Load a classification dataset from a directory.

Parameters:

Name	Type	Description	Default
dataset_path	str	The path to the dataset directory.	required
max_length	int	The maximum length for tokenization. Defaults to 512.	512

Returns:

Name	Type	Description
Dataset	Optional[Dataset]	The loaded dataset.

Raises:

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

Supported Data Formats and Structures:

JSONL

Each line is a JSON object representing an example.

{"text": "The text content"}

CSV

Should contain 'text' columns.

text
"The text content"

Parquet

Should contain 'text' columns.

JSON

An array of dictionaries with 'text' keys.

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

XML

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

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

YAML

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

- text: "The text content"

TSV

Should contain 'text' columns separated by tabs.

Excel (.xls, .xlsx)

Should contain 'text' columns.

SQLite (.db)

Should contain a table with 'text' columns.

Feather

Should contain 'text' columns.