Instructions to use faisalq/arapoembert-base-sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faisalq/arapoembert-base-sentiment-analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="faisalq/arapoembert-base-sentiment-analyzer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("faisalq/arapoembert-base-sentiment-analyzer") model = AutoModel.from_pretrained("faisalq/arapoembert-base-sentiment-analyzer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from faisalq/arapoembert-base-sentiment-analyzer: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/faisalq/arapoembert-base-sentiment-analyzer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://faisalq/arapoembert-base-sentiment-analyzer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/faisalq/arapoembert-base-sentiment-analyzer/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- cedf5a795d462b31f201990615a3bf0d930dded1fc6a7454fe525aa4d285e0dd
- Size of remote file:
- 440 MB
- SHA256:
- 07f47163a303793eef32ff1ddbc852a2877cb3e12bee8dfa925a3b5baece319e
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