To use Haystack for text classification, you can leverage its pipeline-based architecture and integration with transformer models. Haystack provides tools to process text documents, apply classification models, and manage results efficiently. While commonly used for question answering and search, it supports classification by treating it as a labeling task where each document is assigned one or more categories based on its content. You’ll typically use a pre-trained transformer model (like BERT or DistilBERT) fine-tuned for classification, which Haystack integrates through its TransformersTextClassifier component. This approach works well for single-label or multi-label classification tasks.
To set up a basic text classification pipeline, start by installing Haystack (pip install farm-haystack) and importing necessary modules. Create a list of Document objects containing your text data. Initialize a TransformersTextClassifier with a model name (e.g., "distilbert-base-uncased-emotion" for emotion detection) and add it to a Haystack Pipeline. For example:
from haystack import Pipeline
from haystack.nodes import TransformersTextClassifier
from haystack.schema import Document
documents = [Document(content="I loved the movie! The acting was brilliant.")]
classifier = TransformersTextClassifier(
model_name_or_path="distilbert-base-uncased-emotion",
top_k=2 # Return top 2 labels
)
pipeline = Pipeline()
pipeline.add_node(component=classifier, name="classifier", inputs=["File"])
results = pipeline.run(documents=documents)
This code processes the document through the classifier, returning predicted labels (e.g., “joy” and “surprise”) with confidence scores stored in the document’s metadata.
You can customize the workflow by adjusting model parameters, preprocessing text, or adding post-processing steps. For instance, modify top_k to control the number of labels returned, or use a different model from Hugging Face Hub. For domain-specific tasks (e.g., medical text), fine-tune a model on your dataset using libraries like Hugging Face Transformers before integrating it into Haystack. To handle large datasets, use Haystack’s DocumentStore (e.g., InMemoryDocumentStore) for efficient storage and retrieval. If you need multi-label classification, ensure your model supports it (e.g., bert-base-multilingual-uncased with a sigmoid output layer) and configure the pipeline accordingly. Haystack’s modular design also lets you combine classification with other steps, like filtering low-confidence predictions or aggregating results across documents.