Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use sobamchan/roberta-base-mean-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sobamchan/roberta-base-mean-100 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sobamchan/roberta-base-mean-100") sentences = [ "A man is jumping unto his filthy bed.", "A young male is looking at a newspaper while 2 females walks past him.", "The bed is dirty.", "The man is on the moon." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sobamchan/roberta-base-mean-100: direct link, hf CLI and curl.
- Browser
- Download file 5.62 kB
-
https://huggingface.co/sobamchan/roberta-base-mean-100/resolve/main/training_args.bin
- Command line
-
hf download hf://sobamchan/roberta-base-mean-100/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sobamchan/roberta-base-mean-100/resolve/main/training_args.bin
5.62 kB
- Xet hash:
- f93266a4281cf43a34a1bf846cb07c127c16924b40d04487e97ae436a8bc67ea
- Size of remote file:
- 5.62 kB
- SHA256:
- f72d7d2027363ce2aada5dcf64eb8ef939beaf1ecdbd8cafe771d7d75f3cb95e
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