Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:11180
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Culture-and-Morality-Lab/psyembedding-bert-large-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Culture-and-Morality-Lab/psyembedding-bert-large-uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Culture-and-Morality-Lab/psyembedding-bert-large-uncased") sentences = [ "I really shy away from early voting narratives, which I got into in another question thats posted here (I think!) The polls could absolutely be missing a meaningful chunk of Trumps support, as was the case in 16 and 20. Though its worth noting that he is polling much better this time around than the last two times. That could be an indication that the issue has been resolved, through some combination of new polling methods and more willingness of Trump supporters to answer polls and state their support for him. (I talked with one pollster who believes he missed in 20 because theyd get Trump voters on the phone but, for whatever reason, they wouldnt say they were voting for him. Now when voters hesitate, this pollster nudges them a little to pick a candidate. He thinks its fixed the problem. Well see.)\n\n\n\nI did write about some of the polling issues here:\n\n[", "Those are ways to cope individually but to actually abolish the institution of capitalist employment relationships there has to be collective action. You can call it however you want, socialism/communism happens to be a banner that people who held these ideas have been fighting under for many many years.", "everyones scared shitless of trump winning. every single one of us is voting again and then add in new voters and R voters going D and its a landslide win.", "I know, that's why I said one of the reasons. The main reason is his uselessness when your team is bad" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!