Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
mteb
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use aspire/acge_text_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aspire/acge_text_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aspire/acge_text_embedding") 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 result/acge_text_embedding_float/MultilingualSentiment.json from aspire/acge_text_embedding: direct link, hf CLI and curl.
- Browser
- Download file 383 Bytes
-
https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F3/result/acge_text_embedding_float/MultilingualSentiment.json
- Command line
-
hf download hf://aspire/acge_text_embedding@refs/pr/3/result/acge_text_embedding_float/MultilingualSentiment.json
-
curl -L -o MultilingualSentiment.json https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F3/result/acge_text_embedding_float/MultilingualSentiment.json
383 Bytes
| { | |
| "dataset_revision": "46958b007a63fdbf239b7672c25d0bea67b5ea1a", | |
| "mteb_dataset_name": "MultilingualSentiment", | |
| "mteb_version": "1.1.2", | |
| "validation": { | |
| "accuracy": 0.7756000000000001, | |
| "accuracy_stderr": 0.004611097724210823, | |
| "evaluation_time": 25.17, | |
| "f1": 0.7753415907474264, | |
| "f1_stderr": 0.006173837892738144, | |
| "main_score": 0.7756000000000001 | |
| } | |
| } |