Instructions to use Kimty/Ner_custom_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kimty/Ner_custom_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Kimty/Ner_custom_data")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Kimty/Ner_custom_data") model = AutoModelForTokenClassification.from_pretrained("Kimty/Ner_custom_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cc0418e55d7699c2d479de3cecf6495a2e818a811aadc7519f9b439c8b9aec3
- Size of remote file:
- 538 MB
- SHA256:
- eea308735e64f2ce191addc3771506a9119b257b6ea3516cd2bf582a1d21dbaa
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