Instructions to use mpalaval/bert-ner-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpalaval/bert-ner-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mpalaval/bert-ner-1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mpalaval/bert-ner-1") model = AutoModelForTokenClassification.from_pretrained("mpalaval/bert-ner-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb94cb830130c0aa82940bfcd2504b8b0296ae1d1ea48dfb26ff848b6e96a4db
- Size of remote file:
- 431 MB
- SHA256:
- 2c73411ed0cb6e77ca211ae0b66feeb11104e3bd7c4ce4a9030084ae3718fc23
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