Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1115217
loss:GISTEmbedLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use hon9kon9ize/bert-large-cantonese-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hon9kon9ize/bert-large-cantonese-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hon9kon9ize/bert-large-cantonese-nli") sentences = [ "呢度係一班唔同背景嘅人嘅遊行,大家都行緊,有個著住綠色衫嘅男人攞住面旗。", "一個男人攞住面旗。", "一個男人攬住佢阿媽,然後去打仗。", "個市集入面有個女售貨員。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +0 -0
- model.safetensors +1 -1
README.md
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1304182568
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:29a228c8317663cdf7e23837e9723368d712c834c11caf2be8332f75bbcc8b1f
|
| 3 |
size 1304182568
|