Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:8000000
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v3-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v3-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v3-small") sentences = [ "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "How much money do people make from YouTube videos with 1 million views?", "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "\"Does the sentence, \"\"I expect to be disappointed,\"\" make sense?\"" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Update special_tokens_map.json
Browse files- special_tokens_map.json +1 -1
special_tokens_map.json
CHANGED
|
@@ -8,7 +8,7 @@
|
|
| 8 |
},
|
| 9 |
"mask_token": {
|
| 10 |
"content": "[MASK]",
|
| 11 |
-
"lstrip":
|
| 12 |
"normalized": false,
|
| 13 |
"rstrip": false,
|
| 14 |
"single_word": false
|
|
|
|
| 8 |
},
|
| 9 |
"mask_token": {
|
| 10 |
"content": "[MASK]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
"normalized": false,
|
| 13 |
"rstrip": false,
|
| 14 |
"single_word": false
|