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README.md

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 ---
+pipeline_tag: sentence-similarity
 license: apache-2.0
+tags:
+- text2vec
+- feature-extraction
+- sentence-similarity
+- transformers
 ---
+# shibing624/text2vec-base-chinese
+This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
+
+It maps sentences to a 768 dimensional dense vector space and can be used for tasks 
+like sentence embeddings, text matching or semantic search.
+
+
+## Evaluation
+For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
+
+- chinese text matching task：
+
+| Model Name | ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | QPS |
+| :---- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| w2v-light-tencent-chinese | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 33.86 | 10283 |
+| paraphrase-multilingual-MiniLM-L12-v2 | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 41.99 | 2371 |
+| text2vec-base-chinese | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | **48.25** | 2572 |
+
+
+## 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-base-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-base-chinese')
+model = BertModel.from_pretrained('shibing624/text2vec-base-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-base-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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
+)
+```
+## 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 = {Xu Ming},
+  title = {text2vec: A Tool for Text to Vector},
+  year = {2022},
+  url = {https://github.com/shibing624/text2vec},
+}
+```

config.json

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+{
+  "_name_or_path": "hfl/chinese-macbert-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.12.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

logs.txt

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+Epoch:0 Valid| corr: 0.794410
+Epoch:0 Valid| corr: 0.691819
+Epoch:1 Valid| corr: 0.722749
+Epoch:2 Valid| corr: 0.735054
+Epoch:3 Valid| corr: 0.738295
+Epoch:4 Valid| corr: 0.739411
+Test | corr: 0.679971
+Epoch:0 Valid| corr: 0.817416
+Epoch:1 Valid| corr: 0.832376
+Epoch:2 Valid| corr: 0.842308
+Epoch:3 Valid| corr: 0.843520
+Epoch:4 Valid| corr: 0.841837
+Test | corr: 0.793495
+Epoch:0 Valid| corr: 0.814648
+Epoch:1 Valid| corr: 0.831609
+Epoch:2 Valid| corr: 0.841678
+Epoch:3 Valid| corr: 0.842387
+Epoch:4 Valid| corr: 0.841435
+Test | corr: 0.794840

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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rust_model.ot

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sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "hfl/chinese-macbert-base", "tokenizer_class": "BertTokenizer"}