Text Classification
Transformers
Safetensors
English
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/mdeberta-v3-base-mrpc-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/mdeberta-v3-base-mrpc-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/mdeberta-v3-base-mrpc-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/mdeberta-v3-base-mrpc-1") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/mdeberta-v3-base-mrpc-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbfb776198d0c0b4170e7acaf0bf5dbd4c653eaf677b8e72996428d703b89e44
- Size of remote file:
- 4.73 kB
- SHA256:
- 061c44c395028812dae5431d5c842630f02bc26a363e85cb9fbffbc734441aaa
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