---
language:
- en
license: apache-2.0
library_name: Transformers
tags:
- nlp
- text-classification
- argilla
- transformers
dataset_name: argilla/emotion
---

<!-- 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. -->

This is a sample model finetuned from prajjwal1/bert-tiny.

## Model training

Training the model using the `ArgillaTrainer`:

```python
# Load the dataset:
dataset = FeedbackDataset.from_huggingface("argilla/emotion")

# Create the training task:
task = TrainingTask.for_text_classification(text=dataset.field_by_name("text"), label=dataset.question_by_name("label"))

# Create the ArgillaTrainer:
trainer = ArgillaTrainer(
    dataset=dataset,
    task=task,
    framework="transformers",
    model="prajjwal1/bert-tiny",
)

trainer.update_config({
    "logging_steps": 1,
    "num_train_epochs": 1,
    "output_dir": "tmp"
})

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. -->

Model trained with `ArgillaTrainer` for demo purposes

- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Finetuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) for demo purposes
- **Language(s) (NLP):** ['en']
- **License:** apache-2.0
- **Finetuned from model [optional]:** prajjwal1/bert-tiny
### Model Sources [optional]

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- **Repository:** N/A


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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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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: 3.10.7
- Argilla: 1.19.0-dev

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