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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MhmdSyd/Embedding-Arabic-English") model = AutoModel.from_pretrained("MhmdSyd/Embedding-Arabic-English", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from MhmdSyd/Embedding-Arabic-English: direct link, hf CLI and curl.
- Browser
- Download file 996 MB
-
https://huggingface.co/MhmdSyd/Embedding-Arabic-English/resolve/main/optimizer.pt
- Command line
-
hf download hf://MhmdSyd/Embedding-Arabic-English/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/MhmdSyd/Embedding-Arabic-English/resolve/main/optimizer.pt
996 MB
- Xet hash:
- a6bff30986ba0be69c47420436e58bfa2adbc437041552e2080fbce5cb929fdb
- Size of remote file:
- 996 MB
- SHA256:
- 277f1bc63e56cf1af6265f17764d03c3635d0b30328d16b8c4314186ae561f2a
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