Instructions to use Vaibhavbrkn/grammer_classiffication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vaibhavbrkn/grammer_classiffication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vaibhavbrkn/grammer_classiffication")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vaibhavbrkn/grammer_classiffication") model = AutoModelForSequenceClassification.from_pretrained("Vaibhavbrkn/grammer_classiffication", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67d394903bb2507efcaa261c39a9870fb1273165a2e9c6fa8191f8d0ac2d6c45
- Size of remote file:
- 438 MB
- SHA256:
- 3b8e4ba13ba7a9db416f53387a9f61a31fc35d547947cc4f7e00e945b757cf10
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