Instructions to use ramonzaca/roberto-base-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramonzaca/roberto-base-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ramonzaca/roberto-base-finetuned-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ramonzaca/roberto-base-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("ramonzaca/roberto-base-finetuned-pos") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbd2d7ef0eb4deb76d9d59e25f3970a711c329d23c901950c733e611eff9c19e
- Size of remote file:
- 502 MB
- SHA256:
- c53e578b8cf6d3147265ad24abdb95deffed4d25cfc6d94df6f3279b6104d5f7
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