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 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 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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication") model = AutoModelForSequenceClassification.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3899de4630f92edcb8feff3c12c50d6d5dd85fa6af37d4ac35309b54f0914048
- Size of remote file:
- 6.27 GB
- SHA256:
- 84fb074ff99002ae0fdf400d08a987471d675f31e7880aad4d8b7d1448aeb1c0
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