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:
- 15028a277a455c416eb0a106521dc6ab1fc0e885276f8039ff92728cdc70ccc7
- Size of remote file:
- 4.73 kB
- SHA256:
- ec44fbb09d78dcf2fb686afe55f3fa738d7000cc78d715e3b88328e372d8ef3e
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