Text Classification
PyTorch
Transformers
English
Transformers
bert
nlp
argilla
text-embeddings-inference
Instructions to use plaguss/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plaguss/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plaguss/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plaguss/test_model") model = AutoModelForSequenceClassification.from_pretrained("plaguss/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 | |
| *Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model.* | |
| --> | |
| <!-- | |
| ### Direct Use | |
| *This section is for the model use without fine-tuning or plugging into a larger ecosystem/app.* | |
| --> | |
| <!-- | |
| ### Downstream Use [optional] | |
| *This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app* | |
| --> | |
| <!-- | |
| ### Out-of-Scope Use | |
| *This section addresses misuse, malicious use, and uses that the model will not work well for.* | |
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| <!-- | |
| ## Bias, Risks, and Limitations | |
| *This section is meant to convey both technical and sociotechnical limitations.* | |
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| ### Recommendations | |
| *This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.* | |
| --> | |
| <!-- | |
| ## Training Details | |
| ### Training Metrics | |
| *Metrics related to the model training.* | |
| --> | |
| <!-- | |
| ### Training Hyperparameters | |
| - **Training regime:** (fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision) | |
| --> | |
| <!-- | |
| ## 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] | |
| - **Compute Region:** [More Information Needed] | |
| - **Carbon Emitted:** [More Information Needed] | |
| --> | |
| ## Technical Specifications [optional] | |
| ### Framework Versions | |
| - Python: 3.10.7 | |
| - Argilla: 1.19.0-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.* | |
| --> | |
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| ## Model Card Contact | |
| *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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