Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ST-PARA-MPNET-M_Finetuned-AR-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/ST-PARA-MPNET-M_Finetuned-AR-1000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000") model = AutoModel.from_pretrained("danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/ST-PARA-MPNET-M_Finetuned-AR-1000/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 299b49440e8cc05dee00cb1a406e5dc812520637a791f83022f2db8e78ece70b
- Size of remote file:
- 1.11 GB
- SHA256:
- f0148ca3ee9e8d1e8047f335d228ab99c711570cc4c569edb690d0cce99b9872
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