Instructions to use kunalr63/simple_transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunalr63/simple_transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kunalr63/simple_transformer")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kunalr63/simple_transformer") model = AutoModelForTokenClassification.from_pretrained("kunalr63/simple_transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d305ac0cbe6a7d53b21a93d3801b9bb4c5fffdf4bdcf06bcd8607559a4b4bc2
- Size of remote file:
- 872 MB
- SHA256:
- 88608f486e14388471e3457fdaddeb4cb86a05184f10a128d6d5278d9559c9f7
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