遇见您的私人法律顾问：智能法律大模型，智能解答您的法律困惑
为了让法律服务深入到每个人的身边，让更多的人能够得到法律帮助，开启了【律知】这个项目, 致力于打造一系列引领法律智能化的大模型。AI 法律模型是一位虚拟法律顾问，具备丰富的法律知识和技能，能够回答法律问题和提供法律建议。
语言模型
- Law-GLM-10B: 基于 GLM-10B 模型, 在 30GB 中文法律数据上进行指令微调.
Name | Params | Language | Corpus | Objective | File | Config |
|---|---|---|---|---|---|---|
GLM-Base | 110M | English | Wiki Book | Token | glm-base-blank.tar.bz2 | model_blocklm_base.sh |
GLM-Large | 335M | English | Wiki Book | Token | glm-large-blank.tar.bz2 | model_blocklm_large.sh |
GLM-Large-Chinese | 335M | Chinese | WuDaoCorpora | Token Sent Doc | glm-large-chinese.tar.bz2 | model_blocklm_large_chinese.sh |
GLM-Doc | 335M | English | Wiki Book | Token Doc | glm-large-generation.tar.bz2 | model_blocklm_large_generation.sh |
GLM-410M | 410M | English | Wiki Book | Token Doc | glm-1.25-generation.tar.bz2 | model_blocklm_1.25_generation.sh |
GLM-515M | 515M | English | Wiki Book | Token Doc | glm-1.5-generation.tar.bz2 | model_blocklm_1.5_generation.sh |
GLM-RoBERTa | 335M | English | RoBERTa | Token | glm-roberta-large-blank.tar.bz2 | model_blocklm_roberta_large.sh |
GLM-2B | 2B | English | Pile | Token Sent Doc | glm-2b.tar.bz2 | model_blocklm_2B.sh |
GLM-10B | 10B | English | Pile | Token Sent Doc | Download | model_blocklm_10B.sh |
GLM-10B-Chinese | 10B | Chinese | WuDaoCorpora | Token Sent Doc | Download | model_blocklm_10B_chinese.sh |
- GLM-模型结果
dev set, single model, single-task finetuning
Model | COPA | WSC | RTE | WiC | CB | MultiRC | BoolQ | ReCoRD |
|---|---|---|---|---|---|---|---|---|
GLM-10B | 98.0 | 95.2 | 93.1 | 75.7 | 98.7/98.2 | 88.1/63.3 | 88.7 | 94.4/94.0 |
DeBERTa-XXLarge-v2 | 97.0 | 93.5 | 87.8/63.6 | 88.3 | 94.1/93.7 |
- Seq2Seq
CNN/Daily Mail (test set, no additional data used)
Model | ROUGE-1 | ROUGE-2 | ROUGE-L |
|---|---|---|---|
GLM-10B | 44.7 | 21.4 | 41.4 |
T5-11B | 43.5 | 21.6 | 40.7 |
PEGASUS-Large | 44.2 | 21.5 | 41.4 |
BART-Large | 44.2 | 21.3 | 40.9 |
XSum (test set, no additional data used)
Model | ROUGE-1 | ROUGE-2 | ROUGE-L |
|---|---|---|---|
GLM-10B | 48.9 | 25.7 | 40.4 |
PEGASUS-Large | 47.2 | 24.6 | 39.3 |
BART-Large | 45.1 | 22.3 | 37.3 |
- Language Modeling
test set, zero-shot
Model | LAMBADA (accuracy) | Wikitext103 (perplexity) |
|---|---|---|
GLM-10B (bi) | 72.35 | 11.33 |
GLM-10B (uni) | 67.18 | 12.22 |
GPT-2 | 52.66 | 17.48 |
Megatron-LM (8.3B) | 66.51 | 10.81 |
Turing-NLG | 67.98 | 10.21 |
2.快速使用部署
推出的语言模型支持 HuggingFace
