Text Classification
Transformers
PyTorch
Arabic
bert
sentiment-analysis
arabic
marbert
twitter
text-embeddings-inference
Instructions to use iMeshal/arabic-sentiment-classifier-marbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iMeshal/arabic-sentiment-classifier-marbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iMeshal/arabic-sentiment-classifier-marbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iMeshal/arabic-sentiment-classifier-marbert") model = AutoModelForSequenceClassification.from_pretrained("iMeshal/arabic-sentiment-classifier-marbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 511ed641faa3e7c4a46f97bc582009a116d3109b1708a86f8d750e929b1b5ca1
- Size of remote file:
- 651 MB
- SHA256:
- ba2c1ac4e103a5ed8f6c8377725d4fdcc9859ac58587d9aa7459272e5e712acd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.