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--- |
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language: "en" |
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thumbnail: "https://github.com/junnyu" |
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tags: |
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- pytorch |
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- electra |
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- roformer |
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- rotary position embedding |
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license: "MIT" |
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datasets: |
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- openwebtext |
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--- |
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# 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型 |
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# 二、 复现结果(dev dataset) |
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|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.| |
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|---|---|---|---|---|---|---|---|---|---| |
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|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36| |
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|**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|86.64|89.61|81.17|88.85|62.71|80.31| |
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# 三、 训练细节 |
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- 数据集 openwebtext |
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- 训练batch_size 256 |
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- 学习率lr 5e-4 |
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- 最大句子长度max_seqlen 128 |
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- 训练total step 50W |
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- GPU RTX3090 |
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- 训练时间总共耗费55h |
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### wandb日志 |
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预训练日志 |
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https://wandb.ai/junyu/electra_rotary_small_pretrain?workspace=user-junyu |
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GLUE微调日志 |
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https://wandb.ai/junyu/electra_rotary_glue_100?workspace=user-junyu |
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# 四、安装 |
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```bash |
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pip install roformer |
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或 |
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pip install git+https://github.com/JunnYu/RoFormer_pytorch.git |
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``` |
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# 五、 使用 |
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```python |
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import torch |
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from roformer import RoFormerModel |
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from transformers import ElectraTokenizer |
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tokenizer = ElectraTokenizer.from_pretrained("junnyu/roformer_small_discriminator") |
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model = RoFormerModel.from_pretrained("junnyu/roformer_small_discriminator") |
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inputs = tokenizer("Beijing is the capital of China.", return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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print(outputs[0].shape) |
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``` |
