Classification

Bases: TextAPI

TextClassificationAPI leveraging Hugging Face's transformers for text classification tasks. This API provides an interface to classify text into various categories like sentiment, topic, intent, etc.

Attributes:

Name	Type	Description
model	AutoModelForSequenceClassification	A Hugging Face model for sequence classification.
tokenizer	AutoTokenizer	A tokenizer for preprocessing text.
hf_pipeline	Pipeline	A Hugging Face pipeline for text classification.

Methods

classify(self): Classifies text using the model and tokenizer. classification_pipeline(self): Classifies text using the Hugging Face pipeline. initialize_pipeline(self): Lazy initialization of the classification pipeline.

Example CLI Usage:

genius TextClassificationAPI rise \
    batch \
        --input_folder ./input \
    batch \
        --output_folder ./output \
    none \
    --id cardiffnlp/twitter-roberta-base-hate-multiclass-latest-lol \
    listen \
        --args \
            model_name="cardiffnlp/twitter-roberta-base-hate-multiclass-latest" \
            model_class="AutoModelForSequenceClassification" \
            tokenizer_class="AutoTokenizer" \
            use_cuda=True \
            precision="float" \
            quantization=0 \
            device_map="cuda:0" \
            max_memory=None \
            torchscript=False \
            endpoint="*" \
            port=3000 \
            cors_domain="http://localhost:3000" \
            username="user" \
            password="password"

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

Initializes the TextClassificationAPI with the necessary configurations for input, output, and state management.

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 API.	required
**kwargs		Additional keyword arguments for extended functionality.	{}

classification_pipeline()

Accepts text input and returns classification results using the Hugging Face pipeline.

This method uses the Hugging Face pipeline for efficient and robust text classification. It's suitable for various classification tasks such as sentiment analysis, topic classification, and intent recognition.

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: A dictionary containing the original input text and the classification results.

Example CURL Request for text classification:

/usr/bin/curl -X POST localhost:3000/api/v1/classification_pipeline             -H "Content-Type: application/json"             -d '{"text": "The movie was fantastic, with great acting and plot."}' | jq

classify()

Accepts text input and returns classification results. The method uses the model and tokenizer to classify the text and provide the likelihood of each class label.

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: A dictionary containing the original input text and the classification scores for each label.

Example CURL Request for text classification:

/usr/bin/curl -X POST localhost:3000/api/v1/classify             -H "Content-Type: application/json"             -d '{
        "text": "tata sons lost a major contract to its rival mahindra motors"
    }' | jq

initialize_pipeline()

Lazy initialization of the Hugging Face pipeline for classification.