After freeze fine-tuning, I obtained two folders: epoch-1-step-180 and epoch-2-step-360. I calculated the F1 score for epoch-2-step-360.
The F1 code is shown below.
`# -- coding:utf-8 --

@project: ChatGLM-Finetuning

@filename: predict_freeze

@author: 刘聪NLP

@zhihu: https://www.zhihu.com/people/LiuCongNLP

@contact: logcongcong@gmail.com

@time: 2023/4/5 11:12

"""
文件说明：

"""
import sys
sys.path.append('/root/autodl-tmp/chatglm2-6b')
import torch
import json
from modeling_chatglm import ChatGLMForConditionalGeneration
from tokenization_chatglm import ChatGLMTokenizer
from tqdm import tqdm
import time
import os
import argparse

def set_args():
parser = argparse.ArgumentParser()
parser.add_argument('--test_path', default='/root/autodl-tmp/ChatGLM-Finetuning/data/spo_0.json', type=str, help='')
parser.add_argument('--device', default='0', type=str, help='')
parser.add_argument('--model_dir',
default="/root/autodl-tmp/epoch-2-step-360/", type=str,
help='')
parser.add_argument('--max_len', type=int, default=768, help='')
parser.add_argument('--max_src_len', type=int, default=450, help='')
parser.add_argument('--prompt_text', type=str,
default="你现在是一个信息抽取模型，请你帮我抽取出关系内容为"性能故障", "部件故障", "组成"和 "检测工具"的相关三元组，三元组内部用"_"连接，三元组之间用\n分割。文本：",
help='')
parser.add_argument('--top_p', type=float, default=0.7, help='')
parser.add_argument('--do_sample', type=bool, default=False, help='')
parser.add_argument('--num_return_sequences', type=int, default=1, help='')
return parser.parse_args()

def main():
args = set_args()
model = ChatGLMForConditionalGeneration.from_pretrained(args.model_dir)
tokenizer = ChatGLMTokenizer.from_pretrained(args.model_dir)
model.half().to("cuda:{}".format(args.device))
model.eval()
save_data = []
f1 = 0.0
max_tgt_len = args.max_len - args.max_src_len - 3
s_time = time.time()
with open(args.test_path, "r", encoding="utf-8") as fh:
for i, line in enumerate(tqdm(fh, desc="iter")):
with torch.no_grad():
sample = json.loads(line.strip())
src_tokens = tokenizer.tokenize(sample["text"])
prompt_tokens = tokenizer.tokenize(args.prompt_text)

            if len(src_tokens) > args.max_src_len - len(prompt_tokens):
                src_tokens = src_tokens[:args.max_src_len - len(prompt_tokens)]

            tokens = prompt_tokens + src_tokens + ["[gMASK]", "<sop>"]
            input_ids = tokenizer.convert_tokens_to_ids(tokens)
            # input_ids = tokenizer.encode("帮我写个快排算法")

            input_ids = torch.tensor([input_ids]).to("cuda:{}".format(args.device))
            generation_kwargs = {
                "min_length": 5,
                "max_new_tokens": max_tgt_len,
                "top_p": args.top_p,
                "temperature": 0.95,
                "do_sample": args.do_sample,
                "num_return_sequences": args.num_return_sequences,
            }

            response = model.generate(input_ids, **generation_kwargs)
            res = []
            for i_r in range(generation_kwargs["num_return_sequences"]):
                outputs = response.tolist()[i_r][input_ids.shape[1]:]
                r = tokenizer.decode(outputs).replace("<eop>", "")
                res.append(r)
            pre_res = [rr for rr in res[0].split("\n") if len(rr.split("_")) == 3]
            real_res = sample["answer"].split("\n")
            same_res = set(pre_res) & set(real_res)
            if len(set(pre_res)) == 0:
                p = 0.0
            else:
                p = len(same_res) / len(set(pre_res))
            r = len(same_res) / len(set(real_res))
            if (p + r) != 0.0:
                f = 2 * p * r / (p + r)
            else:
                f = 0.0
            f1 += f
            save_data.append(
                {"text": sample["text"], "ori_answer": sample["answer"], "gen_answer": res[0], "f1": f})

e_time = time.time()
print("总耗时：{}s".format(e_time - s_time))
print(f1 / 50)
save_path = os.path.join(args.model_dir, "ft_pt_answer.json")
fin = open(save_path, "w", encoding="utf-8")
json.dump(save_data, fin, ensure_ascii=False, indent=4)
fin.close()

if name == 'main':
main()'
However, the results are shown as follows.
[27:55,.54s/it A decoder onlyzeneration results.padding sideleftwhen initialarchitecture icight paddingwas detected!ForcorrectpleasesetA decoder only architecture is being used, but right padding was detected! For correct generation results, please setpadding side 'leftinitializing the tokenizer.wheniter: 1439it 27:56,107s/it A decoder only architectureis beingdetected! For correcteneration results,padding side 'left’ when initialrigit padcingDleasesetA decoder only architecture is being used, but right padding waspadding side’leftwhen initializing the tokenizer.results..n ease setiter: 144lit [27:56,1.16s/it]总耗时:1676.2034997940063s
0.0

Do you know any possible reasons? If you could reply, I would greatly appreciate it.