Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use NeuML/bert-tiny-sts-last-pooling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/bert-tiny-sts-last-pooling with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/bert-tiny-sts-last-pooling") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - sentence-transformers | |
| - sentence-similarity | |
| - feature-extraction | |
| - dense | |
| - generated_from_trainer | |
| - loss:CosineSimilarityLoss | |
| base_model: google/bert_uncased_L-2_H-128_A-2 | |
| datasets: | |
| - sentence-transformers/stsb | |
| pipeline_tag: sentence-similarity | |
| library_name: sentence-transformers | |
| # Model Card for BERT Tiny with last token pooling | |
| This model is for testing last token pooling. | |