Instructions to use samrawal/bert-large-uncased_med-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samrawal/bert-large-uncased_med-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="samrawal/bert-large-uncased_med-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("samrawal/bert-large-uncased_med-ner") model = AutoModelForTokenClassification.from_pretrained("samrawal/bert-large-uncased_med-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09a7de14e23c8cf28f5a856af6cf3fa92595f8eac6c2fdf774888d32715e9a55
- Size of remote file:
- 1.34 GB
- SHA256:
- cfef2dcf3530f5cf712299146d1f01826395fc2794a8fb402657ddf4bcfc243e
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