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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

二、 复现结果(dev dataset)

Model CoLA SST MRPC STS QQP MNLI QNLI RTE Avg.
ELECTRA-Small-OWT(original) 56.8 88.3 87.4 86.8 88.3 78.9 87.9 68.5 80.36
ELECTRA-RoFormer-Small-OWT (this) 55.76 90.45 87.3 86.64 89.61 81.17 88.85 62.71 80.31

三、 训练细节

  • 数据集 openwebtext
  • 训练batch_size 256
  • 学习率lr 5e-4
  • 最大句子长度max_seqlen 128
  • 训练total step 50W
  • GPU RTX3090
  • 训练时间总共耗费55h

四、wandb日志

五、 使用

import torch
from transformers import ElectraTokenizer,RoFormerForMaskedLM

text = "Beijing is the capital of [MASK]."
tokenizer = ElectraTokenizer.from_pretrained("junnyu/roformer_small_generator")
pt_model = RoFormerForMaskedLM.from_pretrained(
    "junnyu/roformer_small_generator")
pt_inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    pt_outputs = pt_model(**pt_inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
    if id == tokenizer.mask_token_id:
        tokens = tokenizer.convert_ids_to_tokens(pt_outputs[i].topk(k=5)[1])
        pt_outputs_sentence += "[" + "||".join(tokens) + "]"
    else:
        pt_outputs_sentence += "".join(
            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))+" "
print(pt_outputs_sentence)
# pytorch:  beijing is the capital of [china||beijing||taiwan||india||shanghai].  
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Dataset used to train junnyu/roformer_small_generator