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:
- ae98264c1ee090e3ec42a7f2b8a4f4d953b45a6d780d6fc5ec8a976fd32dfbf8
- Size of remote file:
- 1.12 GB
- SHA256:
- 712ed467907d370f9315797dc7862f304ae31ec9db3906f0f037667370a48797
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