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
Download 1_Pooling/config.json from tomaarsen/bert-base-uncased-multi-task: direct link, hf CLI and curl.
- Browser
- Download file 305 Bytes
-
https://huggingface.co/tomaarsen/bert-base-uncased-multi-task/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://tomaarsen/bert-base-uncased-multi-task/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/tomaarsen/bert-base-uncased-multi-task/resolve/main/1_Pooling/config.json
305 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |