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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d1e864476684166632341095171788fafe08c94da7029e33c8729b6f4bbfee9
- Size of remote file:
- 651 MB
- SHA256:
- 60284fac4a2d4cb16382d02be9a918a5508a7324382e991e58fd1e6e111ec231
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