# Load the dataset:
dataset = FeedbackDataset.from_argilla("...")

# Create the training task:
def formatting_func(sample):
    text = sample["text"]
    label = sample["label"][0]["value"]
    return(text, label)

task = TrainingTask.for_text_classification(formatting_func=formatting_func)

# Create the ArgillaTrainer:
trainer = ArgillaTrainer(
    dataset=dataset,
    task=task,
    framework="transformers",
    model="bert-base-cased",
)

trainer.update_config({
    "evaluation_strategy": "epoch",
    "logging_dir": "./logs",
    "logging_steps": 1,
    "num_train_epochs": 1,
    "output_dir": "textcat_model_transformers",
    "use_mps_device": true
})

trainer.train(output_dir="None")

You can test the type of predictions of this model like so:

trainer.predict("This is awesome!")

Model Details

Model Description

Developed by: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Technical Specifications [optional]

Framework Versions

Python: 3.9.17
Argilla: 1.21.0-dev

argilla/end2end_agnews

Model Card for Model ID

Model training

Model Details

Model Description

Technical Specifications [optional]

Framework Versions

argilla/
end2end_agnews