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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,90 @@
 ---
 license: apache-2.0
+pipeline_tag: text-classification
+tags:
+  - transformers
+  - sentence-transformers
+  - text-embeddings-inference
+language:
+  - ko
+  - multilingual
 ---
+
+
+# upskyy/ko-reranker-8k
+
+**ko-reranker-8k**는 [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) 모델에 [한국어 데이터](https://huggingface.co/datasets/upskyy/ko-wiki-reranking)를 finetuning 한 model 입니다.
+
+## Usage
+## Using FlagEmbedding
+```
+pip install -U FlagEmbedding
+```
+
+Get relevance scores (higher scores indicate more relevance):
+
+```python
+from FlagEmbedding import FlagReranker
+
+
+reranker = FlagReranker('upskyy/ko-reranker-8k', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score) # -8.3828125
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+score = reranker.compute_score(['query', 'passage'], normalize=True)
+print(score) # 0.000228713314721116
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores) # [-11.2265625, 8.6875]
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
+print(scores) # [1.3315579521758342e-05, 0.9998313472460109]
+```
+
+
+## Using Huggingface transformers
+
+Get relevance scores (higher scores indicate more relevance):
+
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained('upskyy/ko-reranker-8k')
+model = AutoModelForSequenceClassification.from_pretrained('upskyy/ko-reranker-8k')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
+
+
+
+## Citation
+
+```bibtex
+@misc{li2023making,
+      title={Making Large Language Models A Better Foundation For Dense Retrieval}, 
+      author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},
+      year={2023},
+      eprint={2312.15503},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+@misc{chen2024bge,
+      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
+      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+      year={2024},
+      eprint={2402.03216},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "BAAI/bge-reranker-v2-m3",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "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,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "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": "4.42.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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+}

tokenizer.json

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tokenizer_config.json

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+{
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+  "clean_up_tokenization_spaces": true,
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+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}