Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
information-retrieval
semantic-search
text-embeddings-inference
Instructions to use BarraHome/vmware-embeddings-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BarraHome/vmware-embeddings-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BarraHome/vmware-embeddings-large-v1") 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] - Notebooks
- Google Colab
- Kaggle
Download Information-Retrieval_evaluation_vmware-dev_results.csv from BarraHome/vmware-embeddings-large-v1: direct link, hf CLI and curl.
- Browser
- Download file 421 Bytes
-
https://huggingface.co/BarraHome/vmware-embeddings-large-v1/resolve/main/Information-Retrieval_evaluation_vmware-dev_results.csv
- Command line
-
hf download hf://BarraHome/vmware-embeddings-large-v1/Information-Retrieval_evaluation_vmware-dev_results.csv
-
curl -L -o Information-Retrieval_evaluation_vmware-dev_results.csv https://huggingface.co/BarraHome/vmware-embeddings-large-v1/resolve/main/Information-Retrieval_evaluation_vmware-dev_results.csv
421 Bytes
| epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100 | |
| -1,-1,0.744,0.914,0.968,0.986,0.744,0.744,0.30466666666666664,0.914,0.1936,0.968,0.0986,0.986,0.8345452380952377,0.872140070790615,0.8352266233766233 | |