Instructions to use leemeng/core-term-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leemeng/core-term-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leemeng/core-term-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("leemeng/core-term-ner-v1") model = AutoModelForTokenClassification.from_pretrained("leemeng/core-term-ner-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89a1b6725120d2c13cfe384bf90774b5dc0df20f8287a2fc8c3a8912026a40c9
- Size of remote file:
- 409 MB
- SHA256:
- f2ae9352b876e1e60b71a0a762affee515d5b16fc9230e023184f844fc294c2a
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