Instructions to use lengocquangLAB/deberta-rte-lemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lengocquangLAB/deberta-rte-lemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lengocquangLAB/deberta-rte-lemma")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lengocquangLAB/deberta-rte-lemma") model = AutoModelForSequenceClassification.from_pretrained("lengocquangLAB/deberta-rte-lemma") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- model.safetensors +1 -1
- tokenizer.json +1 -1
model.safetensors
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tokenizer.json
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