zhpmatrix / Bertem
Licence: apache-2.0
论文实现(ACL2019)：《Matching the Blanks: Distributional Similarity for Relation Learning》
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实现说明
主要实现文章前半部分的工作，PyTorch实现，基于huggingface的工作，PyTorch才是世界上最屌的框架，逃。
实现参考
代码说明
（1）主要修改：modeling.py
output representation: BertForSequenceClassification
input representation: BertEmbeddings
input和output都实现了多种策略，可以结合具体的任务，找到最佳的组合。
（2）非主要实现：examples下的关于classification的文件
（3）服务部署：基于Flask，可以在本地开启一个服务。具体实现在tacred_run_infer.py中。
（4）代码仅供参考，不提供数据集，不提供预训练模型，不提供训练后的模型（希望理解吧）。
（5）相关工作可以参考我的博客-神经关系抽取，可能比这个代码更有价值一些吧。
实现结果：
数据集TACRED上的结果：
| 模型序号 | 输入类型 | 输出类型 | 指标类型 | P | R | F1 | 备注 |
|---|---|---|---|---|---|---|---|
| 0 | entity marker | sum(entity start) | micro | 0.68 | 0.63 | 0.65 | base-model,lr=3e-5,epoch=3 |
| macro | 0.60 | 0.54 | 0.55 | ||||
| 1 | entity marker | sum(entity start) | micro | 0.70 | 0.62 | 0.65 | large-model,lr=3e-5,epoch=1 |
| macro | 0.63 | 0.52 | 0.55 | ||||
| -1 | None | None | micro | 0.69 | 0.66 | 0.67 | 手误之后，再也找不到了，尴尬 |
| macro | 0.58 | 0.50 | 0.53 |
数据集SemEval2010 Task 8上的结果：
| 模型序号 | 输入类型 | 输出类型 | 指标类型 | P | R | F1 | 备注 |
|---|---|---|---|---|---|---|---|
| 0 | entity marker | maxpool(entity emb)+relu | micro | 0.86 | 0.86 | 0.86 | bert-large |
| macro | 0.82 | 0.83 | 0.82 |
混合精度加速结果
在具体任务上，延续之前的setting，将train和dev合并共同作为新的train集，test集不变。在fp32 和fp16的两种setting下，比较相同batch_size下，一个epoch的用时或者每个迭代的用时。
| 比较方面 | fp32 | fp16 | 备注 |
|---|---|---|---|
| 训练阶段 | 1.04it/s | 4.41it/s | 12.76it/s（独占显卡） |
| 推断阶段 | 4.14it/s | 8.63it/s | |
| 测试集指标 | 0.65/0.55 | 0.64/0.53 | 格式：micro/macro |
| 模型大小 | 421M | 212M |
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