Instructions to use joeddav/distilbert-base-uncased-go-emotions-student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joeddav/distilbert-base-uncased-go-emotions-student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joeddav/distilbert-base-uncased-go-emotions-student")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joeddav/distilbert-base-uncased-go-emotions-student") model = AutoModelForSequenceClassification.from_pretrained("joeddav/distilbert-base-uncased-go-emotions-student") - Inference
- Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-go-emotions-student
Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this script. It was trained with mixed precision for 10 epochs and otherwise used the default script arguments.
Intended Usage
The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label classification to create psuedo-labels.
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