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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: Transformers |
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tags: |
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- nlp |
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- text-classification |
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- argilla |
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- transformers |
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dataset_name: argilla/emotion |
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--- |
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<!-- This model card has been generated automatically according to the information the `ArgillaTrainer` had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Model Card for *Model ID* |
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This model has been created with [Argilla](https://docs.argilla.io), trained with *Transformers*. |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is a sample model finetuned from prajjwal1/bert-tiny. |
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## Model training |
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Training the model using the `ArgillaTrainer`: |
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```python |
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# Load the dataset: |
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dataset = FeedbackDataset.from_huggingface("argilla/emotion") |
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# Create the training task: |
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task = TrainingTask.for_text_classification(text=dataset.field_by_name("text"), label=dataset.question_by_name("label")) |
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# Create the ArgillaTrainer: |
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trainer = ArgillaTrainer( |
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dataset=dataset, |
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task=task, |
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framework="transformers", |
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model="prajjwal1/bert-tiny", |
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) |
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trainer.update_config({ |
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"logging_steps": 1, |
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"num_train_epochs": 1, |
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"output_dir": "tmp" |
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}) |
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trainer.train(output_dir="None") |
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``` |
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You can test the type of predictions of this model like so: |
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```python |
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trainer.predict("This is awesome!") |
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``` |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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Model trained with `ArgillaTrainer` for demo purposes |
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- **Developed by:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** Finetuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) for demo purposes |
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- **Language(s) (NLP):** ['en'] |
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- **License:** apache-2.0 |
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- **Finetuned from model [optional]:** prajjwal1/bert-tiny |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** N/A |
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## Uses |
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*Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model.* |
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### Direct Use |
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*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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*This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app* |
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### Out-of-Scope Use |
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*This section addresses misuse, malicious use, and uses that the model will not work well for.* |
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## Bias, Risks, and Limitations |
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*This section is meant to convey both technical and sociotechnical limitations.* |
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### Recommendations |
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*This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.* |
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## Training Details |
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### Training Metrics |
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*Metrics related to the model training.* |
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### Training Hyperparameters |
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- **Training regime:** (fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision) |
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--> |
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<!-- |
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## Environmental Impact |
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*Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly* |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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--> |
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## Technical Specifications [optional] |
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### Framework Versions |
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- Python: 3.10.7 |
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- Argilla: 1.19.0-dev |
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## Citation [optional] |
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*If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.* |
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### BibTeX |
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## Glossary [optional] |
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*If relevant, include terms and calculations in this section that can help readers understand the model or model card.* |
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## Model Card Authors [optional] |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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