Instructions to use KevSun/IELTS_essay_scoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevSun/IELTS_essay_scoring with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KevSun/IELTS_essay_scoring")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KevSun/IELTS_essay_scoring") model = AutoModelForSequenceClassification.from_pretrained("KevSun/IELTS_essay_scoring", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f9323b70d024b4bfbd69783b85694ce23acc8c1c1e1be4399fe2e4853ffc53a
- Size of remote file:
- 4.34 kB
- SHA256:
- ecb4655c4b7a25ffc7709237ad76e564f49027b5e8f0799f1c49adb7c6141141
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