Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
Generated from Trainer
dataset_size:5749
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RomainDarous/monoSts_meanPooling_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/monoSts_meanPooling_mistranslationModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/monoSts_meanPooling_mistranslationModel") sentences = [ "The man talked to a girl over the internet camera.", "A group of elderly people pose around a dining table.", "A teenager talks to a girl over a webcam.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from RomainDarous/monoSts_meanPooling_mistranslationModel: direct link, hf CLI and curl.
- Browser
- Download file 341 Bytes
-
https://huggingface.co/RomainDarous/monoSts_meanPooling_mistranslationModel/resolve/main/modules.json
- Command line
-
hf download hf://RomainDarous/monoSts_meanPooling_mistranslationModel/modules.json
-
curl -L -o modules.json https://huggingface.co/RomainDarous/monoSts_meanPooling_mistranslationModel/resolve/main/modules.json
341 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| } | |
| ] |