Instructions to use MENG21/stud-fac-eval-bert-large-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/stud-fac-eval-bert-large-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/stud-fac-eval-bert-large-uncased", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MENG21/stud-fac-eval-bert-large-uncased") model = AutoModelForSequenceClassification.from_pretrained("MENG21/stud-fac-eval-bert-large-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 694566258957f1db08fd96527a6d5bfda5057d8931f68ac693ddc8f9229eebd4
- Size of remote file:
- 1.34 GB
- SHA256:
- a8a5fd25a0c42f6401112019fdbc60ce93bcc2fc9788e987796e8191d6fd52fd
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