Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:16000
loss:DenoisingAutoEncoderLoss
text-embeddings-inference
Instructions to use KiViDrag/pretrain_emotion2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KiViDrag/pretrain_emotion2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KiViDrag/pretrain_emotion2") sentences = [ "can so hopeless to who cares", "id done that though it kind of did a on me and i found myself sympathizing with the demons as the church called them and feeling more disgusted with the people who were supposed to be trying to fight them off", "i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake", "i feel quite honored to exhibit my work in portugal especially within the critical and philosophical context of the god factor project said west" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle