Instructions to use dslim/bert-base-NER-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-base-NER-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-base-NER-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER-uncased") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): bb10ed3
upload flax model
Browse files- flax_model.msgpack +3 -0
flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:16354f635ac87a7523fb1979c8ad2f790a1729b2b96bbb9dde91164b02c2fcd8
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size 435601418
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