Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
loss:SoftmaxLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/bert-base-uncased-multi-task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/bert-base-uncased-multi-task with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/bert-base-uncased-multi-task") sentences = [ "the guy is dead", "The dog is dead.", "Men are sitting in the park.", "People are outside." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |