Instructions to use HooshvareLab/bert-fa-base-uncased-sentiment-digikala with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-sentiment-digikala with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HooshvareLab/bert-fa-base-uncased-sentiment-digikala")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-digikala") model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-digikala") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a3eec7f2570942ca220af115b3122209e1c0650ddb0ac4c22dbcfeca23b0d3d
- Size of remote file:
- 651 MB
- SHA256:
- c80b622569cfb11c6592949b508dc8ba8d46f318c9906625651a7912d566c3b9
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