Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Arabic
English
bert
text-embeddings-inference
Instructions to use MhmdSyd/Embedding-Arabic-English with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MhmdSyd/Embedding-Arabic-English with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MhmdSyd/Embedding-Arabic-English")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MhmdSyd/Embedding-Arabic-English") model = AutoModel.from_pretrained("MhmdSyd/Embedding-Arabic-English") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cc4d2f6f8f591278fb1e24f6f34259390958a886afbd2569ccc8ab7d847ca9f
- Size of remote file:
- 498 MB
- SHA256:
- 775dcb30e9d0bc3f71f386809de67a324a2965b45e520185205f3ef87ab4ed9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.