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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a62b117cccae46afe182743e429f663ec2287486209df695765cb5170ad28a6
- Size of remote file:
- 649 MB
- SHA256:
- e0bb62aa1fcb515a8df3448f1879a2740e5c79c9105ba1d31501c379393fbe80
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