Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use danfeg/AraBERT_Finetuned-AR-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/AraBERT_Finetuned-AR-2000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/AraBERT_Finetuned-AR-2000") 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_Finetuned-AR-2000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/AraBERT_Finetuned-AR-2000") model = AutoModel.from_pretrained("danfeg/AraBERT_Finetuned-AR-2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from danfeg/AraBERT_Finetuned-AR-2000: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/danfeg/AraBERT_Finetuned-AR-2000/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/AraBERT_Finetuned-AR-2000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/AraBERT_Finetuned-AR-2000/resolve/main/model.safetensors
1.48 GB
- Xet hash:
- ed2ef8e7a9365e449818e8f1c0b914e37e4437c5d630b1a9d3dadc5b081481fa
- Size of remote file:
- 1.48 GB
- SHA256:
- c91f40910e2d39fba6e740fc5b74d137de83ea496072c89eddb9bee85df05bbe
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