105 lines
4.2 KiB
Markdown
105 lines
4.2 KiB
Markdown
---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- semantic-search
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- chinese
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---
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# DMetaSoul/sbert-chinese-qmc-finance-v1-distill
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此模型是之前[开源金融问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-finance-v1)的蒸馏轻量化版本（仅4层 BERT），适用于**金融领域的问题匹配**场景，比如：
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- 8千日利息400元? VS 10000元日利息多少钱
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- 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
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- 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
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离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 5% 左右（具体结果详见下文评估小节）。
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# Usage
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## 1. Sentence-Transformers
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通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
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```
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pip install -U sentence-transformers
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```
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然后使用下面的代码来载入该模型并进行文本表征向量的提取：
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["到期不能按时还款怎么办", "剩余欠款还有多少？"]
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model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-finance-v1-distill')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## 2. HuggingFace Transformers
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如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ["到期不能按时还款怎么办", "剩余欠款还有多少？"]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-finance-v1-distill')
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model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-finance-v1-distill')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation
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这里主要跟蒸馏前对应的 teacher 模型作了对比：
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*性能：*
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| | Teacher | Student | Gap |
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| ---------- | --------------------- | ------------------- | ----- |
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| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
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| Cost | 23s | 12s | -47% |
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| Latency | 38ms | 20ms | -47% |
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| Throughput | 418 sentence/s | 791 sentence/s | 1.9x |
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*精度：*
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| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
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| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
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| **Teacher** | 77.40% | 74.55% | 36.00% | 75.75% | 73.24% | 11.58% | 54.75% | 57.61% |
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| **Student** | 75.02% | 71.99% | 32.40% | 67.06% | 66.35% | 7.57% | 49.26% | 52.80% |
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| **Gap** (abs.) | - | - | - | - | - | - | - | -4.81% |
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*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
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## Citing & Authors
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E-mail: xiaowenbin@dmetasoul.com |