Instructions to use minsuas/Misconceptions_FisrtModule with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minsuas/Misconceptions_FisrtModule with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minsuas/Misconceptions_FisrtModule")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minsuas/Misconceptions_FisrtModule") model = AutoModelForSequenceClassification.from_pretrained("minsuas/Misconceptions_FisrtModule") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2776a63364d0bfdb7b7393b06c2b8d58e600b0ced59df1645c414bb776ac640
- Size of remote file:
- 413 kB
- SHA256:
- e596fb28ce8feec6e7f95ee2a5c86c7fc813d248c8ad4d501175ccd0266a79ba
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