Instructions to use sobamchan/sentence-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sobamchan/sentence-t5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sobamchan/sentence-t5-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from sobamchan/sentence-t5-base: direct link, hf CLI and curl.
- Browser
- Download file 505 Bytes
-
https://huggingface.co/sobamchan/sentence-t5-base/resolve/main/modules.json
- Command line
-
hf download hf://sobamchan/sentence-t5-base/modules.json
-
curl -L -o modules.json https://huggingface.co/sobamchan/sentence-t5-base/resolve/main/modules.json
505 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "custom_transformer.LayerSpecificTransformer", | |
| "kwargs": [ | |
| "layer_idx" | |
| ] | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |