mirror of https://github.com/THUDM/ChatGLM-6B
Add training for chat data
rainatam
parent
a9fc018444
commit
5865924cc6
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@ -58,11 +58,14 @@ bash evaluate.sh
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### 评估结果
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| | P-tuning v2 | LoRA |
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| ------- | ----------- | ----- |
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| BLEU-4 | 7.71 | 6.13 |
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| Rouge-1 | 31.35 | 28.36 |
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| Rouge-2 | 7.19 | 4.38 |
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| Rouge-l | 25.17 | 17.54 |
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| ------------- | ----------- | ----- |
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| BLEU-4 | 7.78 | 6.13 |
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| Rouge-1 | 31.34 | 28.36 |
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| Rouge-2 | 7.34 | 4.38 |
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| Rouge-l | 25.26 | 17.54 |
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| Training Loss | 3.8016 | 3.36 |
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#### 实验设置
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@ -98,8 +101,60 @@ learning_rate=5e-4
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## 使用自己的数据集
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修改 `train.sh` 和 `evaluate.sh` 中的 `train_file`、`validation_file`和`test_file`为你自己的 JSON 格式数据集路径，并将 `prompt_column` 和 `response_column` 改为 JSON 文件中输入文本和输出文本对应的 KEY。
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## 对话数据集
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如需要使用多轮对话数据对模型进行微调，可以提供聊天历史，例如
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```json
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{
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"prompt": "是的。上下水管都好的",
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"response": "那就要检查线路了，一般风扇继电器是由电脑控制吸合的，如果电路存在断路，或者电脑坏了的话会出现继电器不吸合的情况！",
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"history": [
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[
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"长城h3风扇不转。继电器好的。保险丝好的传感器新的风扇也新的这是为什么。就是继电器缺一个信号线",
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"用电脑能读数据流吗？水温多少"
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],
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[
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"95",
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"上下水管温差怎么样啊？空气是不是都排干净了呢？"
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]
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]
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}
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```
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训练时需要指定 `--history_column` 为数据中聊天历史的 key（在此例子中是 `history`），将自动把聊天历史拼接，例如：
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- Input
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```
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[Round 0]
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问:长城h3风扇不转。继电器好的。保险丝好的传感器新的风扇也新的这是为什么。就是继电器缺一个信号线
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答:用电脑能读数据流吗?水温多少
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[Round 1]
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问:95
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答:上下水管温差怎么样啊?空气是不是都排干净了呢?
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[Round 2]
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问:是的。上下水管都好的
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```
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- Label
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```
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那就要检查线路了,一般风扇继电器是由电脑控制吸合的,如果电路存在断路,或者电脑坏了的话会出现继电器不吸合的情况!
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```
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要注意超过输入长度 `max_source_length` 的内容会被截。
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可以参考以下指令：
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```shell
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bash train_chat.sh
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```
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## TODO
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* [ ] Support for chat data
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* [x] Support for chat data
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* [ ] Support for full finetuning
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## 引用
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@ -80,6 +80,10 @@ class DataTrainingArguments:
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default=None,
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metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
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)
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history_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the history of chat."},
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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
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)
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@ -135,6 +135,7 @@ def main():
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# Get the column names for input/target.
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prompt_column = data_args.prompt_column
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response_column = data_args.response_column
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history_column = data_args.history_column
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# Temporarily set max_target_length for training.
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max_target_length = data_args.max_target_length
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@ -143,7 +144,16 @@ def main():
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inputs, targets = [], []
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for i in range(len(examples[prompt_column])):
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if examples[prompt_column][i] and examples[response_column][i]:
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inputs.append(examples[prompt_column][i])
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query = examples[prompt_column][i]
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if history_column is None or len(examples[history_column][i]) == 0:
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prompt = query
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else:
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prompt = ""
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history = examples[history_column][i]
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for i, (old_query, response) in enumerate(history):
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prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
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prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
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inputs.append(prompt)
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targets.append(examples[response_column][i])
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inputs = [prefix + inp for inp in inputs]
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@ -167,7 +177,17 @@ def main():
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}
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for i in range(len(examples[prompt_column])):
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if examples[prompt_column][i] and examples[response_column][i]:
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prompt, answer = examples[prompt_column][i], examples[response_column][i]
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query, answer = examples[prompt_column][i], examples[response_column][i]
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if history_column is None:
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prompt = query
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else:
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prompt = ""
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history = examples[history_column][i]
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for i, (old_query, response) in enumerate(history):
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prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
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prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
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prompt = prefix + prompt
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a_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
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b_ids = tokenizer.encode(text=answer, add_special_tokens=False)
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@ -218,6 +238,8 @@ def main():
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desc="Running tokenizer on train dataset",
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)
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print_dataset_example(train_dataset[0])
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print_dataset_example(train_dataset[2])
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exit()
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if training_args.do_eval:
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max_target_length = data_args.val_max_target_length
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@ -0,0 +1,27 @@
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PRE_SEQ_LEN=8
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LR=1e-2
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CUDA_VISIBLE_DEVICES=0 python3 main.py \
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--do_train \
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--train_file $CHAT_TRAIN_DATA \
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--validation_file $CHAT_VAL_DATA \
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--prompt_column prompt \
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--response_column response \
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--history_column history \
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--overwrite_cache \
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--model_name_or_path THUDM/chatglm-6b \
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--output_dir $CHECKPOINT_NAME \
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--overwrite_output_dir \
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--max_source_length 256 \
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--max_target_length 256 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 16 \
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--predict_with_generate \
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--max_steps 3000 \
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--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate $LR \
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--pre_seq_len $PRE_SEQ_LEN \
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--quantization_bit 4
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