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
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pipeline_tag: text-classification
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pipeline_tag: text-classification
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tags:
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- formal or informal classification
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- sentiment-analysis
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- text: Bitte geh einkaufen.
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- text: Können Sie mir spontan dabei helfen?
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- text: Als nächstes kommen 4g Champignons und 500g Mehl dazu.
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library_name: transformers
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