Sentence Similarity
sentence-transformers
PyTorch
Chinese
bert
feature-extraction
text-embeddings-inference
Instructions to use oldhu/text2vec-lert-base-5-tasks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use oldhu/text2vec-lert-base-5-tasks with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("oldhu/text2vec-lert-base-5-tasks") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from oldhu/text2vec-lert-base-5-tasks: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/oldhu/text2vec-lert-base-5-tasks/resolve/main/modules.json
- Command line
-
hf download hf://oldhu/text2vec-lert-base-5-tasks/modules.json
-
curl -L -o modules.json https://huggingface.co/oldhu/text2vec-lert-base-5-tasks/resolve/main/modules.json
230 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] | |