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--- |
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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 |
