Instructions to use pmthangk09/bert-base-uncased-superglue-multirc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pmthangk09/bert-base-uncased-superglue-multirc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pmthangk09/bert-base-uncased-superglue-multirc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pmthangk09/bert-base-uncased-superglue-multirc") model = AutoModelForSequenceClassification.from_pretrained("pmthangk09/bert-base-uncased-superglue-multirc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a45fe0360c4f62ccc593561f065027311ce66dc9fbdd01bf4575b5bf161b5bf
- Size of remote file:
- 438 MB
- SHA256:
- d420178077ea8a404dba9b84b360f7152ac2a987ec2401db212e05970e8ad5b9
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