Fine-tune other models#

After collecting the responses from our FeedbackDataset we can start fine-tuning our basic models. Due to the customizability of the FeedbackDataset, this might require setting up a custom post-processing workflow but we will provide some good toy examples for the text classification task. We will add additional support for other tasks in the future.

Generally, this is as easy as one-two-three but does slightly differ per task.

  1. First, we define a unification strategy for responses to questions we want to use.

  2. Next, we then define a task-mapping. This mapping defines which fields and questions we want to use from our dataset for the downstream training task. These mappings are then used for retrieving data from a dataset and initializing the training.

  3. Lastly, we initialize the ArgillaTrainer and forward the task mapping, unification strategies and training framework.

Text classification#

Background#

Text classification is a widely used NLP task where labels are assigned to text. Major companies rely on it for various applications. Sentiment analysis, a popular form of text classification, assigns labels like 🙂 positive, 🙁 negative, or 😐 neutral to text. Additionally, we distinguish between single- and multi-label text classification.

Single-label#

Single-label text classification refers to the task of assigning a single category or label to a given text sample. Each text is associated with only one predefined class or category. For example, in sentiment analysis, a single-label text classification task would involve assigning labels such as “positive,” “negative,” or “neutral” to individual texts based on their sentiment.

Multi-label#

Multi-label text classification is generally more complex than single-label classification due to the challenge of determining and predicting multiple relevant labels for each text. It finds applications in various domains, including document tagging, topic labeling, and content recommendation systems.

Training#

Data for the training text classification using our FeedbackDataset is defined by following three easy steps.

  1. We need to define a unification strategy RatingStrategy, a LabelStrategy or a MultiLabelStrategy.

  2. For this task, we assume we need a text-label-pair for defining a text classification task. We allow mapping for creating a TrainingTaskMapping.for_text_classification by mapping *Field to a text-value and allow for mapping a RatingStrategy, LabelStrategy or a MultiLabelStrategy to a label-value.

  3. We then define an ArgillaTrainer instance with support for “openai”, “setfit”, “peft”, “spacy” and “transformers”.

Unify responses#

Argilla *Questions need to be unified using a strategy and so do RatingQuestionss, LabelQuestions and MultiLabelQuestions. Therefore, records need to be unified by using a strategy, which takes one of the questions and one of their associated strategies. Luckily this is integrated within the TrainingTaskMapping-step underneath, but you can also do this individually as shown here.

Note

A brief shortcut that RatingQuestions can be unified using a “majority”-, “min”-, “max”- or “disagreement”-strategy. Both LabelQuestions and MultiLabelQuestions can be resolved using a “majority”-, or “disagreement”-strategy.

Define a task mapping#

Now we know which unification strategy to apply, we can now define our TrainingTaskMapping.for_text_classification.

from argilla.feedback import FeedbackDataset, TrainingTaskMapping

dataset = FeedbackDataset.from_huggingface(
    repo_id="argilla/stackoverflow_feedback_demo"
)
task_mapping = TrainingTaskMapping.for_text_classification(
    text=dataset.field_by_name("title"),
    label=dataset.question_by_name("title_question_fit"), # LabelQuestion
    label_strategy=None # default to "majority", or use "disagreement"
)

from argilla.feedback import FeedbackDataset, TrainingTaskMapping

dataset = FeedbackDataset.from_huggingface(
    repo_id="argilla/stackoverflow_feedback_demo"
)
task_mapping = TrainingTaskMapping.for_text_classification(
    text=dataset.field_by_name("title"),
    label=dataset.question_by_name("tags"), # MultiLabelQuestion
    label_strategy=None # default to "majority", or use "disagreement"
)

from argilla.feedback import FeedbackDataset, TrainingTaskMapping

dataset = FeedbackDataset.from_huggingface(
    repo_id="argilla/stackoverflow_feedback_demo"
)
task_mapping = TrainingTaskMapping.for_text_classification(
    text=dataset.field_by_name("title"),
    label=dataset.question_by_name("answer_quality"), # RatingQuestion
    label_strategy=None # default to "majority", or use "min", "max", "disagreement"
)

Use ArgillaTrainer#

Next, we can use our FeedbackDataset and TrainingTaskMappingForTextClassification to initialize our argilla.ArgillaTrainer. We support the frameworks “openai”, “setfit”, “peft”, “spacy” and “transformers”.

Note

This is a newer version and can be imported via from argilla.feedback import ArgillaTrainer. The old trainer can be imported via from argilla.training import ArgillaTrainer. Our docs, contain some additional information on usage of the ArgillaTrainer.

from argilla.feedback import ArgillaTrainer, FeedbackDataset, TrainingTaskMapping

dataset = FeedbackDataset.from_huggingface(
    repo_id="argilla/stackoverflow_feedback_demo"
)
task_mapping = TrainingTaskMapping.for_text_classification(
    text=dataset.field_by_name("title"),
    label=dataset.question_by_name("tags")
)
trainer = ArgillaTrainer(
    dataset=dataset,
    task_mapping=task_mapping,
    framework="setfit",
    fetch_records=False
)
trainer.update_config(num_train_epochs=2)
trainer.train(output_dir="my_awesone_model")

Note

The FeedbackDataset also allows for custom workflows via the prepare_for_training() method.

task_mapping = ...
dataset = rg.FeedbackDataset.from_huggingface(
    repo_id="argilla/stackoverflow_feedback_demo"
)
dataset.prepare_for_training(
    framework="setfit",
    task_mapping=task_mapping
)

An end-to-end example#

Below you can also find an end-to-end example of how to use the ArgillaTrainer.

from argilla.feedback import (
    ArgillaTrainer,
    FeedbackDataset,
    FeedbackRecord,
    LabelQuestion,
    TextField,
    TrainingTaskMapping,
)

dataset = FeedbackDataset(
    guidelines="Add some guidelines for the annotation team here.",
    fields=[
        TextField(name="text", title="Human prompt"),
    ],
    questions =[
        LabelQuestion(
            name="relevant",
            title="Is the response relevant for the given prompt?",
            labels=["yes","no"],
            required=True,
        )
    ]
)
dataset.add_records(
    records=[
        FeedbackRecord(
            fields={"text": "What is your favorite color?"},
            responses=[{"values": {"relevant": {"value": "no"}}}]
        ),
        FeedbackRecord(
            fields={"text": "What do you think about the new iPhone?"},
            responses=[{"values": {"relevant": {"value": "yes"}}}]
        ),
        FeedbackRecord(
            fields={"text": "What is your feeling about the technology?"},
            responses=[{"values": {"relevant": {"value": "yes"}}},
                       {"values": {"relevant": {"value": "no"}}},
                       {"values": {"relevant": {"value": "yes"}}}]
        ),
        FeedbackRecord(
            fields={"text": "When do you expect to buy a new phone?"},
            responses=[{"values": {"relevant": {"value": "no"}}},
                       {"values": {"relevant": {"value": "yes"}}}]
        )

    ]
)

task_mapping = TrainingTaskMapping.for_text_classification(
    text=dataset.field_by_name("text"),
    label=dataset.question_by_name("relevant")
)

trainer = ArgillaTrainer(
    dataset=dataset,
    task_mapping=task_mapping,
    framework="setfit",
    fetch_records=False
)
trainer.update_config(num_train_epochs=2)
trainer.train(output_dir="my_awesone_model")