Instructions to use mschwab/va_bert_tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mschwab/va_bert_tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mschwab/va_bert_tagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mschwab/va_bert_tagger") model = AutoModelForTokenClassification.from_pretrained("mschwab/va_bert_tagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 186097cd5657dda8cbd01ee7aa05fc3089d4d4cb2e426054b78e3fdbb11d5b13
- Size of remote file:
- 3.57 kB
- SHA256:
- 228db3fcfe83324c9811fb8f479e04513ec14fdc04f4418006d6f1b8c6e7ff96
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