Token Classification
Transformers
PyTorch
Safetensors
English
bert
software engineering
ner
named-entity recognition
Instructions to use taidng/wikiser-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taidng/wikiser-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="taidng/wikiser-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("taidng/wikiser-bert-base") model = AutoModelForTokenClassification.from_pretrained("taidng/wikiser-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caed248a8e5e8dcf15783090c9e1f541980e40a9c803253de0688628ea6ae8c9
- Size of remote file:
- 431 MB
- SHA256:
- 4d9a057beb04d3b5d8a793018b2f7bae0c7b77a756d003cc58afd5486038d4fb
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