Instructions to use JayBDev/my_finetuned_wnut_model_1012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayBDev/my_finetuned_wnut_model_1012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="JayBDev/my_finetuned_wnut_model_1012")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("JayBDev/my_finetuned_wnut_model_1012") model = AutoModelForTokenClassification.from_pretrained("JayBDev/my_finetuned_wnut_model_1012") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5a774bf6e0323a889cc3e78d05b52ca307ba229bfd11ad825ad980bbe59389b
- Size of remote file:
- 431 MB
- SHA256:
- 7b8273d20207d5a1cb7ebd06f5c02efca15bbd1b58da00b63e14201252f6572e
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