Text Classification
Transformers
Safetensors
English
roberta
distractor-evaluation
multiple-choice-questions
reading-comprehension
education
regression
text-embeddings-inference
Instructions to use bilalghanem/DISTO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalghanem/DISTO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bilalghanem/DISTO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bilalghanem/DISTO") model = AutoModelForSequenceClassification.from_pretrained("bilalghanem/DISTO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download DISTO_EDM2024.pdf from bilalghanem/DISTO: direct link, hf CLI and curl.
- Browser
- Download file 560 kB
-
https://huggingface.co/bilalghanem/DISTO/resolve/main/DISTO_EDM2024.pdf
- Command line
-
hf download hf://bilalghanem/DISTO/DISTO_EDM2024.pdf
-
curl -L -o DISTO_EDM2024.pdf https://huggingface.co/bilalghanem/DISTO/resolve/main/DISTO_EDM2024.pdf
560 kB
- Xet hash:
- 0679346755efb364b85be98721abef2299a60aaeb7a1d01dba9bcad6493e8ecd
- Size of remote file:
- 560 kB
- SHA256:
- a84b07935788fd3a5da01fbf46368bf7d5a6c851b36aa2acc8412096e3f9ba0e
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