Instructions to use qandos0/SentimentArEng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qandos0/SentimentArEng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qandos0/SentimentArEng")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qandos0/SentimentArEng") model = AutoModelForSequenceClassification.from_pretrained("qandos0/SentimentArEng") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da6d06446e136208f670a119aa488bc5710becae9c186451576a46a6d1f7ce95
- Size of remote file:
- 4.66 kB
- SHA256:
- 080f6bed48816d46381700044e528908b18c25371caa96cf28903f53a6bd3be0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.