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Model: shibing624/text2vec-bge-large-chinese Source: Original Platform
2026-05-14 12:53:42 +08:00
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--- a/1_Pooling/config.json
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+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}
--- a/README.md
+++ b/README.md
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+---
+pipeline_tag: sentence-similarity
+license: apache-2.0
+tags:
+- text2vec
+- feature-extraction
+- sentence-similarity
+- transformers
+- sentence-transformers
+- onnx
+language:
+- zh
+metrics:
+- spearmanr
+library_name: sentence-transformers
+---
+# shibing624/text2vec-bge-large-chinese
+This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-bge-large-chinese.
+
+It maps sentences to a 1024 dimensional dense vector space and can be used for tasks 
+like sentence embeddings, text matching or semantic search.
+
+- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset
+- base model: https://huggingface.co/BAAI/bge-large-zh-noinstruct
+- max_seq_length: 256
+- best epoch: 4
+- sentence embedding dim: 1024
+
+## Evaluation
+For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
+
+### Release Models
+- 本项目release模型的中文匹配评测结果：
+
+
+
+| Arch       | BaseModel                                                   | Model                                                                                                                                             | ATEC  |  BQ   | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc |    Avg    |  QPS  |
+|:-----------|:------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
+| Word2Vec   | word2vec                                                    | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html)                                                                    | 20.00 | 31.49 | 59.46 | 2.57  | 55.78 |  55.04  |  20.70  |   35.03   | 23769 |
+| SBERT      | xlm-roberta-base                                            | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 |  63.01  |  52.28  |   46.46   | 3138  |
+| CoSENT     | hfl/chinese-macbert-base                                    | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese)                                                       | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 |  70.27  |  50.42  |   51.61   | 3008  |
+| CoSENT     | hfl/chinese-lert-large                                      | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)                                                   | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 |  73.01  |  59.04  |   53.12   | 2092  |
+| CoSENT     | nghuyong/ernie-3.0-base-zh                                  | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)                                     | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 |  70.60  |  53.08  |   59.87   | 3089  |
+| CoSENT     | nghuyong/ernie-3.0-base-zh                                  | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)                                 | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 |  76.70  |  63.30  | **63.08** | 3066  |
+| CoSENT     | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)                                             | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 |  68.88  |  51.17  |   53.67   | 3138  |
+| CoSENT     | BAAI/bge-large-zh-noinstruct                                | [shibing624/text2vec-bge-large-chinese](https://huggingface.co/shibing624/text2vec-bge-large-chinese)                                             | 38.41 | 61.34 | 71.72 | 35.15 | 76.44 |  71.81  |  63.15  |   59.72   |  844  |
+
+
+说明：
+- 结果评测指标：spearman系数
+- `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用
+- `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
+- `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用
+- `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集[shibing624/nli-zh-all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset)训练得到，并在中英文测试集评估相对于原模型效果有提升，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用
+- `shibing624/text2vec-bge-large-chinese`模型，是用CoSENT方法训练，基于`BAAI/bge-large-zh-noinstruct`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)训练得到，并在中文测试集评估相对于原模型效果有提升，在短文本区分度上提升明显，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
+- `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
+- 各预训练模型均可以通过transformers调用，如MacBERT模型：`--model_name hfl/chinese-macbert-base` 或者roberta模型：`--model_name uer/roberta-medium-wwm-chinese-cluecorpussmall`
+- 为测评模型的鲁棒性，加入了未训练过的SOHU测试集，用于测试模型的泛化能力；为达到开箱即用的实用效果，使用了搜集到的各中文匹配数据集，数据集也上传到HF datasets[链接见下方](#数据集)
+- 中文匹配任务实验表明，pooling最优是`EncoderType.FIRST_LAST_AVG`和`EncoderType.MEAN`，两者预测效果差异很小
+- 中文匹配评测结果复现，可以下载中文匹配数据集到`examples/data`，运行 [tests/model_spearman.py](https://github.com/shibing624/text2vec/blob/master/tests/model_spearman.py) 代码复现评测结果
+- QPS的GPU测试环境是Tesla V100，显存32GB
+
+模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md)
+
+## Usage (text2vec)
+Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
+
+```
+pip install -U text2vec
+```
+
+Then you can use the model like this:
+
+```python
+from text2vec import SentenceModel
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+
+model = SentenceModel('shibing624/text2vec-bge-large-chinese')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: 
+
+First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+Install transformers:
+```
+pip install transformers
+```
+
+Then load model and predict:
+```python
+from transformers import BertTokenizer, BertModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Load model from HuggingFace Hub
+tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-bge-large-chinese')
+model = BertModel.from_pretrained('shibing624/text2vec-bge-large-chinese')
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Usage (sentence-transformers)
+[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
+
+Install sentence-transformers:
+```
+pip install -U sentence-transformers
+```
+
+Then load model and predict:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+m = SentenceTransformer("shibing624/text2vec-bge-large-chinese")
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+
+sentence_embeddings = m.encode(sentences)
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Full Model Architecture
+```
+CoSENT(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ErnieModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_mean_tokens': True})
+)
+```
+
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained https://huggingface.co/BAAI/bge-large-zh-noinstruct model. 
+Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each 
+possible sentence pairs from the batch.
+We then apply the rank loss by comparing with true pairs and false pairs.
+
+
+## Citing & Authors
+This model was trained by [text2vec](https://github.com/shibing624/text2vec). 
+        
+If you find this model helpful, feel free to cite:
+```bibtex 
+@software{text2vec,
+  author = {Ming Xu},
+  title = {text2vec: A Tool for Text to Vector},
+  year = {2023},
+  url = {https://github.com/shibing624/text2vec},
+}
+```
--- a/config.json
+++ b/config.json
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+{
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+  "transformers_version": "4.31.0",
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+  "use_cache": true,
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+}
--- a/eval_results.txt
+++ b/eval_results.txt
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+eval_pearson = 0.5551856961372815
+eval_spearman = 0.5470897314949114
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--- a/onnx/tokenizer.json
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--- a/onnx/tokenizer_config.json
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+}
--- a/onnx/vocab.txt
+++ b/onnx/vocab.txt
--- a/pytorch_model.bin
+++ b/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a15a7e95aa1c02184301c06d8bfd93921b9a007515e8f864432d19ffbd5e500
+size 1302221545
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
--- a/training_progress_scores.csv
+++ b/training_progress_scores.csv
@@ -0,0 +1,6 @@
+global_step,train_loss,eval_spearman,eval_pearson
+6217,1.187222957611084,0.5114652838626846,0.4941802088748416
+12434,0.5630741119384766,0.529277507678798,0.5185274121904764
+18651,0.4950772523880005,0.4936530600465713,0.4916064648614785
+24868,0.06126270815730095,0.5470897314949114,0.5551856961372815
+31085,0.09893279522657394,0.5114652838626846,0.5117443179915746
--- a/vocab.txt
+++ b/vocab.txt