Text Classification
Transformers
Safetensors
English
deberta-v2
education
tutoring
dialogue
talk-moves
accountable-talk
evaluation
text-embeddings-inference
Instructions to use QuantumLearningMachines/qlm-nto-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantumLearningMachines/qlm-nto-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QuantumLearningMachines/qlm-nto-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QuantumLearningMachines/qlm-nto-classifier") model = AutoModelForSequenceClassification.from_pretrained("QuantumLearningMachines/qlm-nto-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66ce8d802ba0eb196ff59144ee681e1f1358afe11dad489b3a70add97178320a
- Size of remote file:
- 738 MB
- SHA256:
- fa0e3c2a272a6fef952fd6f82397982beee041125834bc63049cdff85f6e04cf
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