Instructions to use Osiris/neutral_non_neutral_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osiris/neutral_non_neutral_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Osiris/neutral_non_neutral_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Osiris/neutral_non_neutral_classifier") model = AutoModelForSequenceClassification.from_pretrained("Osiris/neutral_non_neutral_classifier") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Introduction:
This model belongs to text-classification. You can check whether the sentence consists any emotion.
Label Explaination:
LABEL_1: Non Neutral (have some emotions)
LABEL_0: Neutral (have no emotion)
Usage:
>>> from transformers import pipeline
>>> nnc = pipeline('text-classification', model='Osiris/neutral_non_neutral_classifier')
>>> nnc("Hello, I'm a good model.")
Accuracy:
We reach 93.98% for validation dataset, and 91.92% for test dataset.
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