Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:133472
loss:SoftmaxLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use cassador/indobert-snli-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cassador/indobert-snli-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cassador/indobert-snli-v1") sentences = [ "Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna merah, bermain bersama dalam permainan Rugby saat hujan.", "Tiga orang berada di dalam perahu.", "seorang pria di atas sepeda", "Tim rugby anak-anak, merah versus hijau bermain di tengah hujan." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from cassador/indobert-snli-v1: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/cassador/indobert-snli-v1/resolve/main/modules.json
- Command line
-
hf download hf://cassador/indobert-snli-v1/modules.json
-
curl -L -o modules.json https://huggingface.co/cassador/indobert-snli-v1/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" | |
| } | |
| ] |