Instructions to use PerSpaCor/bert-base-multilingual-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PerSpaCor/bert-base-multilingual-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="PerSpaCor/bert-base-multilingual-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("PerSpaCor/bert-base-multilingual-uncased") model = AutoModelForTokenClassification.from_pretrained("PerSpaCor/bert-base-multilingual-uncased") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("PerSpaCor/bert-base-multilingual-uncased")
model = AutoModelForTokenClassification.from_pretrained("PerSpaCor/bert-base-multilingual-uncased")Quick Links
this model is specifically trained for PerSpaCor a Persian text space corrector you can use this model in https://perspacor.ir additional codes for running the model are found in https://github.com/MatinEbrahimkhani/PerSpaCor_components
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="PerSpaCor/bert-base-multilingual-uncased")