Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/mdeberta-v3-base-vsfc-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/mdeberta-v3-base-vsfc-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-vsfc-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-1") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-vsfc-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe9d655d1d1193b81c5a8772df4886cea0fb17e9841ebea51a15973f772a658f
- Size of remote file:
- 4.73 kB
- SHA256:
- 1fa46c7ee5b6ef3170a9cc763e819de1bb8dec4720487dfd76acecb2c6e258ec
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