Instructions to use MDDDDR/bert_large_uncased_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MDDDDR/bert_large_uncased_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MDDDDR/bert_large_uncased_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MDDDDR/bert_large_uncased_NER") model = AutoModelForTokenClassification.from_pretrained("MDDDDR/bert_large_uncased_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9014c777e99a849887250ef9ab10abb89af46dcb8601d70fffc67b661d166d59
- Size of remote file:
- 1.34 GB
- SHA256:
- 329cc84962bf421d6152c62230d69666fb77371db585c6609ba6ff882b6fe084
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