Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/mdeberta-v3-base-vsmec-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/mdeberta-v3-base-vsmec-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-vsmec-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-vsmec-1") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-vsmec-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f3219af483ce9bdcba0c038e2a0ca0138f392c74f35fc87cb51e05dc92748a8
- Size of remote file:
- 16.3 MB
- SHA256:
- b7d51c69dda72567aa01b744bd108918d9e4af628893799b68af8c6fa44d0682
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