Instructions to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="m3hrdadfi/albert-fa-base-v2-sentiment-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary") model = AutoModelForSequenceClassification.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3ac6f8e58e0c0486031d2270399be008d781696272b72a3cae813a800af3db6
- Size of remote file:
- 1.17 kB
- SHA256:
- f89847b805f889b63d7542c855816538b0a62349d7e92ed9442a8f3b5cfd8410
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