---
library_name: Transformers
tags:
- text-classification
- transformers
- argilla
---

<!-- This model card has been generated automatically according to the information the `ArgillaTrainer` had access to. You
should probably proofread and complete it, then remove this comment. -->

# Model Card for *Model ID*

This model has been created with [Argilla](https://docs.argilla.io), trained with *Transformers*.

<!-- Provide a quick summary of what the model is/does. -->



## Model training

Training the model using the `ArgillaTrainer`:

```python
# 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:

```python
trainer.predict("This is awesome!")
```

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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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## Uses

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### Direct Use

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## Training Details

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### Training Hyperparameters

- **Training regime:** (fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision)
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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Framework Versions

- Python: 3.9.17
- Argilla: 1.21.0-dev

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