Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
bert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:497901
loss:Contrastive
text-embeddings-inference
Instructions to use NeuML/pylate-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/pylate-bert-tiny with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="NeuML/pylate-bert-tiny") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - ColBERT | |
| - PyLate | |
| - sentence-transformers | |
| - sentence-similarity | |
| - feature-extraction | |
| - generated_from_trainer | |
| - dataset_size:497901 | |
| - loss:Contrastive | |
| base_model: google/bert_uncased_L-2_H-128_A-2 | |
| datasets: | |
| - sentence-transformers/msmarco-bm25 | |
| pipeline_tag: sentence-similarity | |
| library_name: PyLate | |
| # Model card for PyLate BERT Tiny | |
| This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. | |
| This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases. | |