Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:49962
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use PerRing/e5_ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PerRing/e5_ko with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PerRing/e5_ko") sentences = [ "query: 잡지는 인종차별적이고 성차별적인 유머를 제공했다.", "passage: 잡지는 인종과 성별에 대해 편견적인 농담을 실었다.", "passage: 잡지는 모든 사람을 존중하는 유머를 제공했다.", "passage: 그들은 중요하지만 활용도가 낮은 데이터를 보유하고 있었다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config.json from PerRing/e5_ko: direct link, hf CLI and curl.
- Browser
- Download file 735 Bytes
-
https://huggingface.co/PerRing/e5_ko/resolve/main/config.json
- Command line
-
hf download hf://PerRing/e5_ko/config.json
-
curl -L -o config.json https://huggingface.co/PerRing/e5_ko/resolve/main/config.json
735 Bytes
| { | |
| "_name_or_path": "/backup/hgyoo/embedding/model/e5_ko/checkpoint-50", | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |