Instructions to use interneuronai/student_progress_tracking_bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/student_progress_tracking_bart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/student_progress_tracking_bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/student_progress_tracking_bart") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/student_progress_tracking_bart") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b00722b684d5307f55a7b143178691d616cbf4fa1bfa1179b636e72c93586ed
- Size of remote file:
- 2.45 GB
- SHA256:
- 46e9f769cfa30c9d8ed435bae32889fd34b4f51825baa8f88d99803f66b1e85b
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