infgrad commited on
Commit
834addb
1 Parent(s): 6d898db

Fix query_prompt_name variable name (#9)

Browse files

- Fix query_prompt_name variable name; update model author (7a25c260c0d6b0ad6204d2579ccd29184929d7b4)

Files changed (1) hide show
  1. README.md +2 -2
README.md CHANGED
@@ -5481,7 +5481,7 @@ from sentence_transformers import SentenceTransformer
5481
 
5482
  # This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
5483
  # They are defined in `config_sentence_transformers.json`
5484
- prompt_name = "s2p_query"
5485
  queries = [
5486
  "What are some ways to reduce stress?",
5487
  "What are the benefits of drinking green tea?",
@@ -5493,7 +5493,7 @@ docs = [
5493
  ]
5494
 
5495
  # !The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
5496
- model = SentenceTransformer("infgrad/stella_en_400M_v5", trust_remote_code=True).cuda()
5497
  query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
5498
  doc_embeddings = model.encode(docs)
5499
  print(query_embeddings.shape, doc_embeddings.shape)
 
5481
 
5482
  # This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
5483
  # They are defined in `config_sentence_transformers.json`
5484
+ query_prompt_name = "s2p_query"
5485
  queries = [
5486
  "What are some ways to reduce stress?",
5487
  "What are the benefits of drinking green tea?",
 
5493
  ]
5494
 
5495
  # !The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
5496
+ model = SentenceTransformer("dunzhang/stella_en_400M_v5", trust_remote_code=True).cuda()
5497
  query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
5498
  doc_embeddings = model.encode(docs)
5499
  print(query_embeddings.shape, doc_embeddings.shape)