Instructions to use maple/bert-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maple/bert-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="maple/bert-large-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("maple/bert-large-cased") model = AutoModelForTokenClassification.from_pretrained("maple/bert-large-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e5b2dc38df7e38335dd979145e2d4dce2d776373eea33bb83782814aa8db25f
- Size of remote file:
- 1.33 GB
- SHA256:
- e6aa8f9f26ab57ae3ca9cd0272908514d980e950796ccf898627a9b538fa6508
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