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 model.safetensors from QuantumLearningMachines/qlm-map-classifier: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/QuantumLearningMachines/qlm-map-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://QuantumLearningMachines/qlm-map-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/QuantumLearningMachines/qlm-map-classifier/resolve/main/model.safetensors
738 MB
- Xet hash:
- 2793a6c379a3c194e51d0b8a849f32c4f2023c00d0e20b90f0984d982126d7db
- Size of remote file:
- 738 MB
- SHA256:
- eb2f6517ee021f17fd95ad9f81c32d344ab05fba35df3b7bfb160a87f39d05b4
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