GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language understanding and generation tasks.
Please refer to our paper for a detailed description of GLM:
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (ACL 2022)
Zhengxiao Du*, Yujie Qian*, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang (*: equal contribution)
Find more examples in our Github repo.
Model description
glm-10b-chinese is pretrained on the WuDaoCorpora dataset. It has 48 transformer layers, with hidden size 4096 and 64 attention heads in each layer. The model is pretrained with autoregressive blank filling objectives designed for natural language understanding, seq2seq, and language modeling.
How to use
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("BAAI/glm-10b-chinese", trust_remote_code=True)
model = model.half().cuda()
inputs = tokenizer("凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。", return_tensors="pt")
inputs = tokenizer.build_inputs_for_generation(inputs, max_gen_length=512)
inputs = {key: value.cuda() for key, value in inputs.items()}
outputs = model.generate(**inputs, max_length=512, eos_token_id=tokenizer.eop_token_id)
print(tokenizer.decode(outputs[0].tolist()))
We use three different mask tokens for different tasks: [MASK] for short blank filling, [sMASK] for sentence filling, and [gMASK] for left to right generation. You can find examples about different masks from here.
Citation
Please cite our paper if you find this code useful for your research:
@article{DBLP:conf/acl/DuQLDQY022,
author = {Zhengxiao Du and
Yujie Qian and
Xiao Liu and
Ming Ding and
Jiezhong Qiu and
Zhilin Yang and
Jie Tang},
title = {{GLM:} General Language Model Pretraining with Autoregressive Blank Infilling},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
May 22-27, 2022},
pages = {320--335},
publisher = {Association for Computational Linguistics},
year = {2022},
}