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
About pooler_type
#12
by huaanrui - opened
Hey! I use this model in transformers AutoModelForSequenceClassification. By default, it uses Bertconfig, and uses the cls token for sentence embedding. In the config.json file, I find "pooler_type": "first_token_transform", while in ./1_Pooling/config.json, "pooling_mode_mean_tokens": true, it seems like you use mean pooler. So what is the final pooler type? Thank you!