Text Classification
Transformers
Safetensors
deberta-v2
formal or informal classification
sentiment-analysis
text-embeddings-inference
Instructions to use LenDigLearn/formality-classifier-mdeberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LenDigLearn/formality-classifier-mdeberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LenDigLearn/formality-classifier-mdeberta-v3-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LenDigLearn/formality-classifier-mdeberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("LenDigLearn/formality-classifier-mdeberta-v3-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98015353f7e6a1ac4ddcc99195459c8f2ab0bdde3ce30e7146a108c4ff2e0044
- Size of remote file:
- 16.4 MB
- SHA256:
- 9ddbe35b768b22d6cd0d61a0a88ea3a188b7c00bbb7b825f9f247cf0b79c2365
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