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:
- a430706367c23451c9cf64cc91398178ae4c9727b2231ec2d8bb63119d29e606
- Size of remote file:
- 1.58 kB
- SHA256:
- ef4fd1adc3366f4d50a6fb03c0ddb5f2c98641b4b733df8031137541614f8b00
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