Instructions to use HooshvareLab/bert-fa-base-uncased-ner-peyma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-ner-peyma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-fa-base-uncased-ner-peyma")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-peyma") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-peyma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8c9a4fe4e2b40d5d76e6b402ac503463d3742942ed759968e0487e418b5202d
- Size of remote file:
- 651 MB
- SHA256:
- 19e4a6e88fb4f17063cce48e30014f555c427e9f1022137fdf306bdf3eb6c95b
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