Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:86450
loss:DenoisingAutoEncoderLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SwarajSolanke-turtle/make-model-mapping-TSDAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SwarajSolanke-turtle/make-model-mapping-TSDAE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SwarajSolanke-turtle/make-model-mapping-TSDAE") sentences = [ "mini", "mini contryman e electric 1", "tata safari xm diesel 1956", "daewoo cielo glx petrol 1498" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from SwarajSolanke-turtle/make-model-mapping-TSDAE: direct link, hf CLI and curl.
- Browser
- Download file 277 Bytes
-
https://huggingface.co/SwarajSolanke-turtle/make-model-mapping-TSDAE/resolve/main/modules.json
- Command line
-
hf download hf://SwarajSolanke-turtle/make-model-mapping-TSDAE/modules.json
-
curl -L -o modules.json https://huggingface.co/SwarajSolanke-turtle/make-model-mapping-TSDAE/resolve/main/modules.json
277 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| } | |
| ] |