Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:942069
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use sobamchan/bert-base-uncased-mean-softmax-350 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sobamchan/bert-base-uncased-mean-softmax-350 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sobamchan/bert-base-uncased-mean-softmax-350") sentences = [ "Two women having drinks and smoking cigarettes at the bar.", "Women are celebrating at a bar.", "Two kids are outdoors.", "The four girls are attending the street festival." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from sobamchan/bert-base-uncased-mean-softmax-350: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/sobamchan/bert-base-uncased-mean-softmax-350/resolve/main/scheduler.pt
- Command line
-
hf download hf://sobamchan/bert-base-uncased-mean-softmax-350/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/sobamchan/bert-base-uncased-mean-softmax-350/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 8c4ef2dd00d117a7e9900e038301b0d9a84af2460c28b5f9066144110a8ecb0f
- Size of remote file:
- 1.06 kB
- SHA256:
- 099475937869f062547807e85f3c18ad992483ffd43226ed64c6038e2d4f2539
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