Instructions to use Amir13/bert-base-parsbert-uncased-wnut2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amir13/bert-base-parsbert-uncased-wnut2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Amir13/bert-base-parsbert-uncased-wnut2017")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Amir13/bert-base-parsbert-uncased-wnut2017") model = AutoModelForTokenClassification.from_pretrained("Amir13/bert-base-parsbert-uncased-wnut2017") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02564047aeb78bf11ae72b76a80fd0a8f4556144450d99295f2449bc294c9af4
- Size of remote file:
- 649 MB
- SHA256:
- b2ff6cf12f3574f91a76e3dd268ebbfecbc710d76d02db9ad0623b86c73ff940
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