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Model: uer/sbert-base-chinese-nli
Source: Original Platform

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README.md

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+---
+language: zh
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+license: apache-2.0
+widget:
+    - source_sentence: "那个人很开心"
+      sentences:
+        - "那个人非常开心"
+        - "那只猫很开心"
+        - "那个人在吃东西"
+---
+
+# Chinese Sentence BERT
+
+## Model description
+
+This is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
+
+## How to use
+
+You can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here: 
+
+```python
+>>> from sentence_transformers import SentenceTransformer
+>>> model = SentenceTransformer('uer/sbert-base-chinese-nli')
+>>> sentences = ['那个人很开心', '那个人非常开心']
+>>> sentence_embeddings = model.encode(sentences)
+>>> from sklearn.metrics.pairwise import paired_cosine_distances
+>>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]])
+```
+
+## Training data
+
+[ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data. 
+
+## Training procedure
+
+The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five epochs with a sequence length of 128 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved.
+
+```
+python3 finetune/run_classifier_siamese.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
+                                           --vocab_path models/google_zh_vocab.txt \
+                                           --config_path models/sbert/base_config.json \
+                                           --train_path datasets/ChineseTextualInference/train.tsv \
+                                           --dev_path datasets/ChineseTextualInference/dev.tsv \
+                                           --learning_rate 5e-5 --epochs_num 5 --batch_size 64
+```
+
+Finally, we convert the pre-trained model into Huggingface's format:
+
+```
+python3 scripts/convert_sbert_from_uer_to_huggingface.py --input_model_path models/finetuned_model.bin \                                                                
+                                                         --output_model_path pytorch_model.bin \                                                                                            
+                                                         --layers_num 12
+```
+
+### BibTeX entry and citation info
+
+```
+@article{reimers2019sentence,
+  title={Sentence-bert: Sentence embeddings using siamese bert-networks},
+  author={Reimers, Nils and Gurevych, Iryna},
+  journal={arXiv preprint arXiv:1908.10084},
+  year={2019}
+}
+
+@article{zhao2019uer,
+  title={UER: An Open-Source Toolkit for Pre-training Models},
+  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
+  journal={EMNLP-IJCNLP 2019},
+  pages={241},
+  year={2019}
+}
+
+@article{zhao2023tencentpretrain,
+  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
+  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
+  journal={ACL 2023},
+  pages={217},
+  year={2023}
+```

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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