Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use danfeg/AraBERT_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/AraBERT_Base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/AraBERT_Base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use danfeg/AraBERT_Base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/AraBERT_Base") model = AutoModel.from_pretrained("danfeg/AraBERT_Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from danfeg/AraBERT_Base: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/danfeg/AraBERT_Base/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/AraBERT_Base/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/AraBERT_Base/resolve/main/model.safetensors
1.48 GB
- Xet hash:
- a58b15db32f557897ac4885c6812e7587c55dbc3e311a1e62ad28dcf6edc0a32
- Size of remote file:
- 1.48 GB
- SHA256:
- 2f7f69a5bba5318162b92a191a123ed67e31ea05a937b394d699b358b6fa425e
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