Instructions to use rlatmddus159/ner2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlatmddus159/ner2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rlatmddus159/ner2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rlatmddus159/ner2") model = AutoModelForTokenClassification.from_pretrained("rlatmddus159/ner2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71c456ca9de30bf32862473eb689f36a497f1811dea076ca8d967a7056d1bddb
- Size of remote file:
- 440 MB
- SHA256:
- 131fbd63de3088688dc9800700cbe01d2a70d072d381f7ef3b68ec15457ba1f5
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