AutoBERT-Zero
AutoBERT-Zero: Evolving BERT Backbone from Scratch Jiahui Gao, Hang Xu, Han shi, Xiaozhe Ren, Philip L.H. Yu, Xiaodan Liang, Xin Jiang, Zhenguo Li
pip install git+https://github.com/JunnYu/AutoBERT-Zero-pytorch
or
pip install autobert
https://wandb.ai/junyu/autobert-small/runs/2flfy8gx
https://wandb.ai/junyu/autobert-small/runs/howc6tps
small
import torch
from transformers import BertTokenizerFast
from autobert import AutoBertModelForMaskedLM
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
for model in ["junnyu/autobert-small-light","junnyu/autobert-small-sdconv"]:
tokenizer = BertTokenizerFast.from_pretrained(model)
model = AutoBertModelForMaskedLM.from_pretrained(model)
model.to(device)
text = "Beijing is the capital of [MASK]."
inputs = tokenizer(text, return_tensors="pt")
inputs.to(device)
# pytorch
with torch.no_grad():
outputs = model(**inputs).logits[0]
pt_outputs_sentence = ""
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
prob, indice = outputs[i].softmax(-1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(indice)
slist = []
for p, t in zip(prob, tokens):
slist.append(t + "+" + str(round(p.item(), 4)))
pt_outputs_sentence += " " + "[ " + " || ".join(slist) + " ]"
else:
pt_outputs_sentence += " " + "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
)
print(pt_outputs_sentence.strip())
# light: beijing is the capital of [ china+0.1801 || india+0.0273 || america+0.0181 || japan+0.0166 || us+0.0143 ] .
# sdconv: beijing is the capital of [ china+0.1533 || india+0.054 || delhi+0.0414 || beijing+0.0389 || london+0.022 ] .