Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ARBERT_Finetuned-EN-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/ARBERT_Finetuned-EN-1000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ARBERT_Finetuned-EN-1000") 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/ARBERT_Finetuned-EN-1000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/ARBERT_Finetuned-EN-1000") model = AutoModel.from_pretrained("danfeg/ARBERT_Finetuned-EN-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from danfeg/ARBERT_Finetuned-EN-1000: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/danfeg/ARBERT_Finetuned-EN-1000/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/ARBERT_Finetuned-EN-1000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/ARBERT_Finetuned-EN-1000/resolve/main/model.safetensors
651 MB
- Xet hash:
- a7d638f094faba840fa937093f7ebd0290e0fed85f5b7f7ff3f3252aca7aa25b
- Size of remote file:
- 651 MB
- SHA256:
- 90b0f2bc9e7692f44ea498ea60329637294536c15e8fdaa6c598a67ed57e73f2
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