Instructions to use ModelTC/bert-base-uncased-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-cola") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55233785b54d1587f328a452a12a36147486aea14f1dcebcc2fa5a282c9e2e2c
- Size of remote file:
- 438 MB
- SHA256:
- 72555ba23e4f03a0f1c1bba150f371208bc8e5cc41c6602a7966000b0ececd1a
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