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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TerminatorPower/nerT") model = AutoModelForSequenceClassification.from_pretrained("TerminatorPower/nerT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from TerminatorPower/nerT: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/TerminatorPower/nerT/resolve/main/model.safetensors
- Command line
-
hf download hf://TerminatorPower/nerT/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/TerminatorPower/nerT/resolve/main/model.safetensors
443 MB
- Xet hash:
- b4728ddab1dd122578806dd9183d3f267909bab6fdf47829ed3d36cb541dbe8c
- Size of remote file:
- 443 MB
- SHA256:
- 11daeeb89ccc28da86821dd62615c2c074088ceb64ec3d3cccd7d37e0768cf59
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