Instructions to use farshadafx/Bert-Sentiment-Fa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use farshadafx/Bert-Sentiment-Fa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="farshadafx/Bert-Sentiment-Fa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("farshadafx/Bert-Sentiment-Fa") model = AutoModelForSequenceClassification.from_pretrained("farshadafx/Bert-Sentiment-Fa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f9f989088cb9e08808073cf25f5dae31cdba60b26515dd2221b626404481642
- Size of remote file:
- 651 MB
- SHA256:
- ef2324eee70c3522861fd4786e051ff81bda026ec13454f56f331a2062ecab99
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