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 training_args.bin from sobamchan/bert-base-uncased-mean-softmax-350: direct link, hf CLI and curl.
- Browser
- Download file 5.62 kB
-
https://huggingface.co/sobamchan/bert-base-uncased-mean-softmax-350/resolve/main/training_args.bin
- Command line
-
hf download hf://sobamchan/bert-base-uncased-mean-softmax-350/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sobamchan/bert-base-uncased-mean-softmax-350/resolve/main/training_args.bin
5.62 kB
- Xet hash:
- 55826d292d2d2e533e96a8fac23d95df3b2fb9cabb22a12e7fbb1e8042587612
- Size of remote file:
- 5.62 kB
- SHA256:
- b62015fae60b7471dfd18d6ef7ba4b3f0c4747a61025747e1455f7a7f2fc37cf
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