Instructions to use Sharpaxis/BERT-NER-CoNLL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharpaxis/BERT-NER-CoNLL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sharpaxis/BERT-NER-CoNLL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Sharpaxis/BERT-NER-CoNLL") model = AutoModelForTokenClassification.from_pretrained("Sharpaxis/BERT-NER-CoNLL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0eedec924af104eb59e878babf41eced8c4d4a92b2398a628f5a13f2538a8465
- Size of remote file:
- 1.34 GB
- SHA256:
- 4f1aa56528ed4b4845a575d24806836ace270e86df537b76bb0e8677ae5724af
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