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
File size: 431 Bytes
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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.
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