Instructions to use HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary 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-deepsentipers-binary 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-deepsentipers-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary") model = AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c87e51556d890594a7f524d3138b45c8620a8f8c780a8bc8fd7f82aebbf625be
- Size of remote file:
- 651 MB
- SHA256:
- b43f85f8e0bf4d1a939d93eebdbb016a82b1606bf1e5ed5ccacbb0fa870d6253
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