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:
- 2395935deb462e788235a2adab058d4b6bf3ded6f9b30e5a72a7fe10fbc419f1
- Size of remote file:
- 1.58 kB
- SHA256:
- e4f35a47380ae8413063d2a66c6b773ceb283c878960caafb54c2c001c79fbc9
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