Instructions to use TerminatorPower/nerT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TerminatorPower/nerT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TerminatorPower/nerT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TerminatorPower/nerT") model = AutoModelForSequenceClassification.from_pretrained("TerminatorPower/nerT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4728ddab1dd122578806dd9183d3f267909bab6fdf47829ed3d36cb541dbe8c
- Size of remote file:
- 443 MB
- SHA256:
- 11daeeb89ccc28da86821dd62615c2c074088ceb64ec3d3cccd7d37e0768cf59
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