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
Download config.json from hon9kon9ize/bert-large-cantonese-nli: direct link, hf CLI and curl.
- Browser
- Download file 639 Bytes
-
https://huggingface.co/hon9kon9ize/bert-large-cantonese-nli/resolve/main/config.json
- Command line
-
hf download hf://hon9kon9ize/bert-large-cantonese-nli/config.json
-
curl -L -o config.json https://huggingface.co/hon9kon9ize/bert-large-cantonese-nli/resolve/main/config.json
639 Bytes
| { | |
| "_name_or_path": "hon9kon9ize/bert-large-cantonese", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 21628 | |
| } | |