Text Classification
Transformers
Safetensors
English
xlnet
automatic-short-answer-grading
regression
education
short-answer
assessment
grading
Eval Results (legacy)
Instructions to use kenzykhaled/XLENT_ASAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenzykhaled/XLENT_ASAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kenzykhaled/XLENT_ASAG")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kenzykhaled/XLENT_ASAG") model = AutoModelForSequenceClassification.from_pretrained("kenzykhaled/XLENT_ASAG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20922bf103bfc3d6a19c61f0993ca093498eccf059d39a47c9996e49bb932346
- Size of remote file:
- 5.3 kB
- SHA256:
- fecb7fd8880f0171d83b7bceea56341321593b0cea8e8df4b89edb1be3c3e327
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