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This project provides a large-scale cleaned Chinese conversation dataset and a Chinese GPT model pre-trained on this dataset. Please refer to our paper for more details.
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Our code used for the pre-training is adapted from the TransferTransfo model based on the Transformers library. The codes used for both pre-training and fine-tuning are provided in this repository.
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本项目提供了一个大规模的经过系统清洗的中文对话数据集，并提供在此数据集上的对话预训练模型，更多信息可参考我们的论文。
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本项目代码修改自TransferTransfo，使用了HuggingFace Pytorch版的Transformers库, 可用于预训练与微调。
We present a Large-scale Cleaned Chinese Conversation corpus (LCCC) containing: LCCC-base and LCCC-large. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
The statistic of our corpus is presented below. Dialogues with only two utterances are regarded as "Single-turn", and dialogues with more than three utterances are regarded as "Multi-turn". The vocabulary size is calculated in word-level, and Jieba is used to tokenize each utterance to words.
| LCCC-base | Single-turn | Multi-turn |
|---|---|---|
| Sessions | 3,354,382 | 3,466,607 |
| Utterances | 6,708,554 | 13,365,268 |
| Characters | 68,559,727 | 163,690,614 |
| Vocabulary | 372,063 | 666,931 |
| Avg. words per utterance | 6.79 | 8.32 |
| Avg. utterances per session | 2 | 3.86 |
Note that LCCC-base is cleaned using more strict rules compared to LCCC-large. A tokenized version of LCCC-base is also provided.
| LCCC-large | Single-turn | Multi-turn |
|---|---|---|
| Sessions | 7,273,804 | 4,733,955 |
| Utterances | 14,547,608 | 18,341,167 |
| Characters | 162,301,556 | 217,776,649 |
| Vocabulary | 662,514 | 690,027 |
| Avg. words per utterance | 7.45 | 8.14 |
| Avg. utterances per session | 2 | 3.87 |
The raw dialogues for LCCC-base originate from a Weibo Corpus that we crawled from Weibo, and the raw dialogues for LCCC-large is built by combining several conversation datasets in addition to the Weibo Corpus:
| Dataset | Sessions | Sample |
|---|---|---|
| Weibo Corpus | 79M | Q:火锅我在重庆成都吃了七八顿火锅 A: 哈哈哈哈！那我的嘴巴 可能要烂掉！ |
| PTT Gossiping Corpus | 0.4M | Q:为什么乡民总是欺负国高中生呢QQ A:如果以为选好科系就会变成比尔盖兹那不如退学吧 |
| Subtitle Corpus | 2.74M | Q:京戏里头的人都是不自由的 A:他们让人拿笼子给套起来了了 |
| Xiaohuangji Corpus | 0.45M | Q:你谈过恋爱么 A:谈过，哎，别提了，伤心.. |
| Tieba Corpus | 2.32M | Q:前排，鲁迷们都起床了吧 A:标题说助攻，但是看了那球，真是活生生的讽刺了 |
| Qingyun Corpus | 0.1M | Q:看来你很爱钱 A:噢是吗？那么你也差不多了 |
| Douban Conversation Corpus | 0.5M | Q:看原版英文电影学纯正英语 A:大爱老友记反复看了好多次 了 Q:一样光盘都快被我看花了 A:那你现在的英语应该不错了 |
| E-commerical Conversation Corpus | 0.5M | Q:这个会不会聚划算 A:暂时没有哦 Q:后期会不会有 A:不一定哦亲多多关注我们哦 |
| Chinese Chat Corpus | 0.5M | Q: 我今天腿都废了，你们过节，我搬砖 A: 辛苦啊，圣诞节还去赚大钱了加油 Q: 毕竟是没男朋友的人，什么节都是一样的 |
We also present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.
Similar to TransferTransfo, we concatenate all dialogue histories into one context sentence, and use this sentence to predict the response. The input of our model consists of word embedding, speaker embedding, and positional embedding of each word.
| Models | Parameter Size | Pre-training Dataset | Description |
|---|---|---|---|
| GPTNovel | 95.5M | Chinese Novel | A GPT model pre-trained on Chinese Novel dataset (1.3B words, note that we do not provide the detail of this model) |
| CDial-GPTLCCC-base | 95.5M | LCCC-base | A GPT model post-trained on LCCC-base dataset from GPTNovel |
| CDial-GPT2LCCC-base | 95.5M | LCCC-base | A GPT2 model post-trained on LCCC-base dataset from GPTNovel |
| CDial-GPTLCCC-large | 95.5M | LCCC-large | A GPT model post-trained on LCCC-large dataset from GPTNovel |
Install from the source codes:
git clone https://github.com/lemon234071/GPT-Chinese.git
cd GPT-Chinese
pip install -r requirements.txt
Step 1: Prepare the data for fine-tuning (E.g., STC dataset or "data/toy_data.json" in our respository) and the pre-trianed model:
wget https://coai-dataset.oss-cn-beijing.aliyuncs.com/STC-corpus.zip # Download the STC dataset and unzip into "data_path" dir (fine-tuning on STC)
wget https://coai-dataset.oss-cn-beijing.aliyuncs.com/GPT_LCCC-large.zip # Download the GPT<sub>LCCC-large</sub> weights file and unzip into "model_checkpoint" dir
Note: If the computer's memory is insufficient, you can process the file into txt format, use the "train_path" to load data in a distributed manner (the function we adapted from Internet), and you need to leave "data_path" empty.
Step 2: Train the model
python train.py --pretrained --model_checkpoint ./models/ --data_path data/STC.json # Single GPU training
python -m torch.distributed.launch --nproc_per_node=8 train.py --pretrained --model_checkpoint ./models/ --data_path data/STC.json # Training on 8 GPUs
Step 3: Inference mode
python infer.py --model_checkpoint ./models/ --datapath data/STC_test.json --out_path STC_result.txt # Do Inference on a corpus
python interact.py --model_checkpoint ./models/ # Interact on the terminal
Training Arguments
| Arguments | Type | Default value | Description |
|---|---|---|---|
| model_checkpoint | str | "" | Path or URL of model files (Directory of pre-training model and config/vocab files) |
| pretrained | bool | False | If False, then train the model from scratch |
| data_path | str | "" | Path of the dataset |
| dataset_cache | str | default="dataset_cache" | Path or url of the dataset cache |
| train_path | str | "" | Path of the training set for distributed dataset |
| valid_path | str | "" | Path of the validation set for distributed dataset |
| log_file | str | "" | Output logs to a file under this path |
| num_workers | int | 1 | Number of subprocesses for data loading |
| n_epochs | int | 70 | Number of training epochs |
| train_batch_size | int | 8 | Batch size for training |
| valid_batch_size | int | 8 | Batch size for validation |
| max_history | int | 15 | Number of previous exchanges to keep in history |
| scheduler | str | "noam" | Method of optimizer |
| n_emd | int | 768 | Number of n_emd in config file (for noam) |
| eval_before_start | bool | False | If true, start evaluation before training |
| warmup_steps | int | 5000 | Warm up steps |
| valid_steps | int | 0 | Perform validation every X steps, if is not 0 |
| gradient_accumulation_steps | int | 64 | Accumulate gradients on several steps |
| max_norm | float | 1.0 | Clipping gradient norm |
| device | str | "cuda" if torch.cuda.is_available() else "cpu" | Device (cuda or cpu) |
| fp16 | str | "" | Set to O0, O1, O2 or O3 for fp16 training (see apex documentation) |
| local_rank | int | -1 | Local rank for distributed training (-1: not distributed) |
Evaluation is performed on results generated by models fine-tuned on STC dataset. All responses are generated using the Nucleus Sampling scheme with a threshold 0.9 and temperature 0.7.
| Models | Model Size | PPL | BLEU-2 | BLEU-4 | Dist-1 | Dist-2 | Greedy Matching | Embedding Average |
|---|---|---|---|---|---|---|---|---|
| Attn-Seq2seq | 73M | 34.20 | 3.93 | 0.90 | 8.5 | 11.91 | 65.84 | 83.38 |
| Transformer | 113M | 22.10 | 6.72 | 3.14 | 8.8 | 13.97 | 66.06 | 83.55 |
| GPT2-chitchat | 88M | - | 2.28 | 0.54 | 10.3 | 16.25 | 61.54 | 78.94 |
| GPTNovel | 95.5M | 21.27 | 5.96 | 2.71 | 8.0 | 11.72 | 66.12 | 83.34 |
| GPTLCCC-base | 95.5M | 18.38 | 6.48 | 3.08 | 8.3 | 12.68 | 66.21 | 83.54 |
| GPT2LCCC-base | 95.5M | 22.76 | 5.69 | 2.50 | 7.7 | 10.87 | 66.24 | 83.46 |
| GPTLCCC-large | 95.5M | 18.23 | 6.63 | 3.20 | 8.3 | 12.71 | 66.25 | 83.63 |
We also employed 3 crowd-sourced annotators to evaluate 200 samples of each model about fluency, relevance, and diversity based on a 2/1/0 score schema. Refer to our paper for more details.
| Models | +2 | +1 | +0 | Kappa |
|---|---|---|---|---|
| Attn-Seq2Seq | 27.1% | 21.4% | 51.4% | 0.4544 |
| Transformer | 42.4% | 23.6% | 34.0% | 0.4429 |
| GPT2-chitchat | 24.3% | 19,1% | 56.6% | 0.3941 |
| GPTLCCC-base | 46.7% | 21.8% | 31.5% | 0.3954 |
| GPTLCCC-large | 48.6% | 24.5% | 27.0% | 0.4203 |
We randomly select 1000 generated samples, and make a statistic of toxic generation:
| Repetition | Dirty word | Other Bad Cases |
|---|---|---|
| 23 | 9 | 2 |
Single-turn generated examples from STC dataset (fine-tuned on STC):
| Post | Response |
|---|---|
| 这 居 然 是 北 京 不 是 哈 尔 滨 。 | 哈 尔 滨 的 天 气 好 像 比 北 京 好 点 ， 我 这 里 就 是 小 雨 ， 白 天 都 是 大 风 |
| 运 营 商 校 园 广 告 大 P K ， 太 欢 乐 了 ！ 哈 哈 哈 。 | 我 以 为 是 马 云 的 广 告 。 |
| D e l s a S p o s a 糖 果 色 系 列 婚 纱 ， 粉 蓝 红 紫 ， 俏 皮 又 清 新 ， 你 喜 欢 吗 ？ | 最 后 一 件 太 美 了 ， 美 的 不 像 话 ！ |
Multi-turn generated examples using the interactive script:
| Roel | Response |
|---|---|
| User | 别爱我没结果 |
| Bot | 没 结 果 的 爱 是 多 么 的 痛 。 |
| User | 但是爱过就可以了 |
| Bot | 有 时 候 ，爱 情 就 是 一 场 游 戏 。 |
| User | 输的总是我 |
| Bot | 我 爱 你 ， 就 像 我 爱 你 一 样 。 |
| User | 你可拉到吧 |
| Bot | 你 可 拉 到 吧 。 |
| User | 别学我说话 |
| Bot | 我 说 话 就 是 你 不 行 。 |
| User | 逼我动手是吧 |
| Bot | 不 ， 是 逼 你 动 口 是 吧 |
Yida Wang, Pei Ke, Yinhe Zheng, Kaili Huang, Yong Jiang, Xiaoyan Zhu, Minlie Huang
The LCCC dataset and the pre-trained models aim to facilitate the research for conversation generation. The LCCC dataset provided in this repository originates from various sources. Although a rigorous cleaning process has been carried out, there is no guarantee that all the inappropriate contents have been completely filtered out. All the contents contained in this dataset does not represent the authors' opinion. This repository contains only part of the modeling machinery needed to actually produce a dialogue model. The decoding script provided in this repository is only for the research purpose. We are not responsible for any contents generated using our model.
Please kindly cite our paper if you use the datasets or models in your research:
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}