Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
Generated from Trainer
dataset_size:300000
loss:DenoisingAutoEncoderLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/E5-base-unsupervised-TSDAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/E5-base-unsupervised-TSDAE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/E5-base-unsupervised-TSDAE") sentences = [ "One mole of a substance of substance such atoms or). The is known or Avogadro's constant", "how effective are birth control pills and pulling out?", "can pvc be phthalate free?", "One mole of a substance is equal to 6.022 × 10²³ units of that substance (such as atoms, molecules, or ions). The number 6.022 × 10²³ is known as Avogadro's number or Avogadro's constant." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from bobox/E5-base-unsupervised-TSDAE: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/bobox/E5-base-unsupervised-TSDAE/resolve/main/modules.json
- Command line
-
hf download hf://bobox/E5-base-unsupervised-TSDAE/modules.json
-
curl -L -o modules.json https://huggingface.co/bobox/E5-base-unsupervised-TSDAE/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |