end2end_agnews

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Model Card for Model ID

This model has been created with Argilla, trained with Transformers.

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Model training

Training the model using the ArgillaTrainer:

# 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

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• 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]

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Technical Specifications [optional]

Framework Versions

• Python: 3.9.17
• Argilla: 1.21.0-dev

<!-- ## Citation [optional] *If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.* ### BibTeX --> <!-- ## Glossary [optional] *If relevant, include terms and calculations in this section that can help readers understand the model or model card.* --> <!-- ## Model Card Authors [optional] *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->