Text Classification
Transformers
PyTorch
English
deberta-v2
logical-reasoning
logical-equivalence
constrastive-learning
text-embeddings-inference
Instructions to use qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2") model = AutoModelForSequenceClassification.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2") - Notebooks
- Google Colab
- Kaggle
6.28 GB
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