---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9623924
- loss:MSELoss
base_model: BAAI/bge-m3
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- negative_mse
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.9378885799751235
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.930037764519436
      name: Spearman Cosine
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: negative_mse
      value: -0.010874464351218194
      name: Negative Mse
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.9378994572414889
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.9300802695581766
      name: Spearman Cosine
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model distilled  from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [tr-sentences](https://huggingface.co/datasets/altaidevorg/tr-sentences) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Refer to the [blog post](https://medium.com/altai-dev/distilling-efficiency-experiments-in-compressing-baai-bge-m3-using-a-synthetic-dataset-9430e21c6b8f) and the [8l variant](https://huggingface.co/altaidevorg/bge-m3-distill-8l) for more information.