Text Classification
Transformers
Safetensors
English
deberta-v2
education
mathematics
misconception-detection
evaluation
text-embeddings-inference
Instructions to use QuantumLearningMachines/qlm-map-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantumLearningMachines/qlm-map-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QuantumLearningMachines/qlm-map-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QuantumLearningMachines/qlm-map-classifier") model = AutoModelForSequenceClassification.from_pretrained("QuantumLearningMachines/qlm-map-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from QuantumLearningMachines/qlm-map-classifier: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/QuantumLearningMachines/qlm-map-classifier/resolve/3dac359bfe9b5fc4245dfdcd427bf857ebc90fbe/tokenizer.json
- Command line
-
hf download hf://QuantumLearningMachines/qlm-map-classifier@3dac359bfe9b5fc4245dfdcd427bf857ebc90fbe/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/QuantumLearningMachines/qlm-map-classifier/resolve/3dac359bfe9b5fc4245dfdcd427bf857ebc90fbe/tokenizer.json
8.66 MB
File too large to display, you can check the raw version instead.