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---
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]
<!-- Provide the basic links for the model. -->
- **Repository:** N/A
<!--
## Uses
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### Direct Use
*This section is for the model use without fine-tuning or plugging into a larger ecosystem/app.*
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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## Training Details
### Training Metrics
*Metrics related to the model training.*
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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
*Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly*
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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [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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## Citation [optional]
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### BibTeX
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## Glossary [optional]
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## Model Card Authors [optional]
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