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finetune_cross_100/build_paws_data.ipynb

@@ -0,0 +1,360 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a460ddd6-72b4-42b9-a923-8416a75ef87e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://hf-mirror.com\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# 设置环境变量\n",
+    "os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n",
+    "\n",
+    "# 打印环境变量以确认设置成功\n",
+    "print(os.environ.get('HF_ENDPOINT'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "18b5c7c9-6f3b-498e-8671-a4cd065d0bd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/maris/miniconda3/envs/dnagpt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
+    "from transformers import Trainer\n",
+    "import evaluate\n",
+    "import numpy as np\n",
+    "from transformers import TrainingArguments\n",
+    "from transformers import AutoModelForSequenceClassification\n",
+    "\n",
+    "\n",
+    "# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象\n",
+    "raw_datasets = load_dataset('paws-x', 'en')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ba9dc8c5-1c9e-47c5-9c77-b06573e05432",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 49401\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fbe3f78a-da0f-4f9a-87fc-4d484b4a3b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': 1,\n",
+       " 'sentence1': 'In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .',\n",
+       " 'sentence2': 'In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .',\n",
+       " 'label': 0}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_datasets[\"train\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7f048fc9-e8ca-4f27-bfb6-46ef8c12bc52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_zh = load_dataset('paws-x', 'zh')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d6128b62-6789-4934-a3dd-2c8fd6e7cb2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 49401\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['id', 'sentence1', 'sentence2', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_datasets_zh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9e936f7d-a0b3-4698-97a9-bd07379cd199",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': 1,\n",
+       " 'sentence1': '1560年10月，他在巴黎秘密会见了英国大使Nicolas Throckmorton，要求他通过苏格兰返回英国。',\n",
+       " 'sentence2': '1560年10月，他在巴黎秘密会见了英国大���尼古拉斯·斯罗克莫顿，并要求他通过英格兰返回苏格兰的护照。',\n",
+       " 'label': 0}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_datasets_zh[\"train\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f3ab940-cfb0-4753-83c1-a8ccc31cce26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_fr = load_dataset('paws-x', 'fr')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a9d707a7-a46f-4b15-b2e6-1c61df79be9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_de = load_dataset('paws-x', 'de')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f9092ac6-6467-4666-bd27-3000c158add5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_es = load_dataset('paws-x', 'es')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8876048c-90c4-4964-9804-115de7128b78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_ja = load_dataset('paws-x', 'ja')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a14dcbc9-73a3-4102-88e5-b94d2cfadb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets_ko = load_dataset('paws-x', 'ko')  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ee6b5d7c-c993-45f9-a008-53e2653818f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_list = []\n",
+    "for index in range(0, 49401):\n",
+    "    data_en = raw_datasets[\"train\"][index]\n",
+    "    data_fr =  raw_datasets_fr[\"train\"][index]\n",
+    "    data_de = raw_datasets_de[\"train\"][index]\n",
+    "    data_zh =  raw_datasets_zh[\"train\"][index]\n",
+    "    data_es = raw_datasets_es[\"train\"][index]\n",
+    "    data_ja =  raw_datasets_ja[\"train\"][index]\n",
+    "    data_ko =  raw_datasets_ko[\"train\"][index]\n",
+    "\n",
+    "    if data_en[\"id\"]==index+1 and data_fr[\"id\"]==index+1 and data_de[\"id\"]==index+1 and data_zh[\"id\"]==index+1 and data_es[\"id\"]==index+1 and data_ja[\"id\"]==index+1 and data_ko[\"id\"]==index+1:\n",
+    "        data = {\n",
+    "                \"data_id\":data_en[\"id\"],\n",
+    "                \"sentence1\":data_en[\"sentence1\"],\n",
+    "                \"sentence1_fr\":data_fr[\"sentence1\"],\n",
+    "                \"sentence1_de\":data_de[\"sentence1\"],\n",
+    "                \"sentence1_zh\":data_zh[\"sentence1\"],\n",
+    "                \"sentence1_es\":data_es[\"sentence1\"],\n",
+    "                \"sentence1_ja\":data_ja[\"sentence1\"],\n",
+    "                \"sentence1_ko\":data_ko[\"sentence1\"],\n",
+    "                \"label\":data_en[\"label\"],\n",
+    "                \"sentence2\":data_en[\"sentence2\"],\n",
+    "                \"sentence2_fr\":data_fr[\"sentence2\"],\n",
+    "                \"sentence2_de\":data_de[\"sentence2\"],\n",
+    "                \"sentence2_zh\":data_zh[\"sentence2\"],\n",
+    "                \"sentence2_es\":data_es[\"sentence2\"],\n",
+    "                \"sentence2_ja\":data_ja[\"sentence2\"],\n",
+    "                \"sentence2_ko\":data_ko[\"sentence2\"],\n",
+    "               }\n",
+    "        data_list.append(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cc4f5c16-ed09-4866-ab49-736e8940f45c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49401"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "afc22ff2-06fe-4999-b72c-208e97dc62cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sql_appbk\n",
+    "sql_appbk.insert_data_list(data_list, \"pawx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "819d9d2c-2ae8-449f-9582-8ff608bd349f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "ins_file = open(\"paws-x-multi-pair.jsonl\", \"w\")\n",
+    "\n",
+    "for item in data_list:\n",
+    "    ins_file.write(json.dumps(item, ensure_ascii=False)+\"\\n\")\n",
+    "\n",
+    "ins_file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baa248d1-090f-4be7-878d-746f10bbb444",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

finetune_cross_100/gpt2_gene_multiv2_ft_de_en.jsonl

@@ -0,0 +1,100 @@
+{"seed": 0, "dna_protein_pair_full": {"accuracy": 0.4445, "f1": 0.11191047162270183}, "dna_protein_pair_rand_full": {"accuracy": 0.5025, "f1": 0.039806996381182146}}
+{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.328, "f1": 0.45232273838630804}, "dna_protein_pair_rand_full": {"accuracy": 0.488125, "f1": 0.5703043022035676}}
+{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.5, "f1": 0.1452991452991453}, "dna_protein_pair_rand_full": {"accuracy": 0.65875, "f1": 0.5694006309148265}}
+{"seed": 3, "dna_protein_pair_full": {"accuracy": 0.49, "f1": 0.015444015444015444}, "dna_protein_pair_rand_full": {"accuracy": 0.50125, "f1": 0.019656019656019656}}
+{"seed": 4, "dna_protein_pair_full": {"accuracy": 0.4955, "f1": 0.05435801312089972}, "dna_protein_pair_rand_full": {"accuracy": 0.503125, "f1": 0.05917159763313609}}
+{"seed": 5, "dna_protein_pair_full": {"accuracy": 0.2935, "f1": 0.397954835960801}, "dna_protein_pair_rand_full": {"accuracy": 0.4625, "f1": 0.44228274967574577}}
+{"seed": 6, "dna_protein_pair_full": {"accuracy": 0.3955, "f1": 0.47503256621797657}, "dna_protein_pair_rand_full": {"accuracy": 0.585625, "f1": 0.6706408345752608}}
+{"seed": 7, "dna_protein_pair_full": {"accuracy": 0.5185, "f1": 0.37181996086105673}, "dna_protein_pair_rand_full": {"accuracy": 0.59375, "f1": 0.5320374370050396}}
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+{"seed": 10, "dna_protein_pair_full": {"accuracy": 0.4605, "f1": 0.3641720683559222}, "dna_protein_pair_rand_full": {"accuracy": 0.55, "f1": 0.4262948207171315}}
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+{"seed": 35, "dna_protein_pair_full": {"accuracy": 0.349, "f1": 0.43440486533449174}, "dna_protein_pair_rand_full": {"accuracy": 0.50875, "f1": 0.5718954248366013}}
+{"seed": 36, "dna_protein_pair_full": {"accuracy": 0.4695, "f1": 0.41542699724517906}, "dna_protein_pair_rand_full": {"accuracy": 0.641875, "f1": 0.6432129514321295}}
+{"seed": 37, "dna_protein_pair_full": {"accuracy": 0.491, "f1": 0.19334389857369255}, "dna_protein_pair_rand_full": {"accuracy": 0.514375, "f1": 0.2528846153846154}}
+{"seed": 38, "dna_protein_pair_full": {"accuracy": 0.4405, "f1": 0.23303632625085674}, "dna_protein_pair_rand_full": {"accuracy": 0.543125, "f1": 0.37892948173322005}}
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finetune_cross_100/gpt2_gene_multiv2_ft_ko_fr.jsonl

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finetune_cross_100/gpt2_gene_multiv2_ft_ko_ja.jsonl

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finetune_cross_100/gpt2_gene_multiv2_ft_nl_nl_test_others.py

@@ -0,0 +1,252 @@
+# import os
+
+# # 设置环境变量
+# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+# # 打印环境变量以确认设置成功
+# print(os.environ.get('HF_ENDPOINT'))
+
+# import subprocess
+# import os
+
+# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True)
+# output = result.stdout
+# for line in output.splitlines():
+#     if '=' in line:
+#         var, value = line.split('=', 1)
+#         os.environ[var] = value
+        
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+from transformers import Trainer
+import evaluate
+import numpy as np
+from transformers import TrainingArguments
+from transformers import AutoModelForSequenceClassification
+import json
+from transformers import set_seed
+import random
+import numpy as np
+import torch
+from tqdm import tqdm
+import sys
+
+
+# seed = 42
+# random.seed(seed)
+# np.random.seed(seed)
+# torch.manual_seed(seed)
+# torch.cuda.manual_seed_all(seed)
+
+
+# 动态生成随机种子
+
+seed = int(sys.argv[1])
+lang1 = sys.argv[2] #语言1
+lang2 = sys.argv[3] #语言2
+
+
+#seed = random.randint(0, 10000)
+#print(f"Generated seed: {seed}")
+set_seed(seed)
+result = {}
+result["seed"] = seed
+
+
+# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象
+raw_datasets = load_dataset('dnagpt/paws-x-multi-pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle
+
+g_lang1_s = "sentence1_"+lang1
+g_lang2_s = "sentence2_"+lang2
+
+# 定义翻转标签的函数
+def flip_data(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example[g_lang1_s]
+    example["sentence2"] = example[g_lang2_s]
+    return example
+
+raw_datasets = raw_datasets.map(flip_data, batched=False)
+
+
+
+#分词器
+tokenizer = AutoTokenizer.from_pretrained("dnagpt/gpt2_gene_multi_v2")
+tokenizer.pad_token = tokenizer.eos_token
+
+
+#分词函数
+def tokenize_function(example):
+    return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length")
+
+#构建分词后的数据集
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+
+#训练数据构建
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+
+#指标函数定义
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return {'accuracy': (predictions==labels).sum() / len(labels)}
+
+
+
+training_args = TrainingArguments(
+    output_dir="ds_job_dna_2222",
+    learning_rate=1e-5,
+    lr_scheduler_type="constant_with_warmup",
+    warmup_ratio=0.1,
+    optim='adamw_torch',
+    weight_decay=0.0,
+    seed=seed,  # 使用动态生成的随机种子
+    per_device_train_batch_size=64,
+    per_device_eval_batch_size=64,
+    num_train_epochs=4, #训练多少轮
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_strategy="epoch",
+    load_best_model_at_end=True
+)
+
+#模型定义，文本分类模型
+model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gpt2_gene_multi_v2", num_labels=2)
+model.config.pad_token_id = model.config.eos_token_id
+
+trainer = Trainer(
+    model,
+    training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["test"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train() #模型训练
+
+
+
+
+#模型测试 蛋白质数据集
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.5) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+    
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+result["dna_protein_pair_full"] = ret
+
+
+
+
+#############################################################
+#模型测试 蛋白质数据集，随机版本
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.2) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+
+result["dna_protein_pair_rand_full"] = ret
+
+
+
+print(json.dumps(result))
+

finetune_cross_100/gpt2_gene_multiv2_ft_zh_de.jsonl

@@ -0,0 +1,100 @@
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finetune_cross_100/gpt2_gene_multiv2_ft_zh_ko.jsonl

@@ -0,0 +1,100 @@
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finetune_cross_100/run_ft_all.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+export HF_ENDPOINT=https://hf-mirror.com
+lang1=$1
+lang2=$2
+for ((i=0;i<100;i++))
+do
+	echo "----------------------------------------------"$i
+	python gpt2_gene_multiv2_ft_nl_nl_test_others.py $i $lang1 $lang2 >> gpt2_gene_multiv2_ft_${lang1}_${lang2}.json
+done

finetune_pt_100/gpt2_gene_eng_zh_de_es_ft_en.jsonl

@@ -0,0 +1,98 @@
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+{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.51, "f1": 0.02}, "dna_protein_pair_rand_full": {"accuracy": 0.501875, "f1": 0.01629227351271291}}

finetune_pt_100/gpt2_gene_eng_zh_de_es_ft_test_others.py

@@ -0,0 +1,237 @@
+# import os
+
+# # 设置环境变量
+# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+# # 打印环境变量以确认设置成功
+# print(os.environ.get('HF_ENDPOINT'))
+
+# import subprocess
+# import os
+
+# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True)
+# output = result.stdout
+# for line in output.splitlines():
+#     if '=' in line:
+#         var, value = line.split('=', 1)
+#         os.environ[var] = value
+        
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+from transformers import Trainer
+import evaluate
+import numpy as np
+from transformers import TrainingArguments
+from transformers import AutoModelForSequenceClassification
+import json
+from transformers import set_seed
+import random
+import numpy as np
+import torch
+from tqdm import tqdm
+import sys
+
+
+# seed = 42
+# random.seed(seed)
+# np.random.seed(seed)
+# torch.manual_seed(seed)
+# torch.cuda.manual_seed_all(seed)
+
+
+# 动态生成随机种子
+
+seed = int(sys.argv[1])
+lang = sys.argv[2] #语言
+
+#seed = random.randint(0, 10000)
+#print(f"Generated seed: {seed}")
+set_seed(seed)
+result = {}
+result["seed"] = seed
+
+
+
+# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象
+raw_datasets = load_dataset('paws-x', lang)  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x
+
+#分词器
+tokenizer = AutoTokenizer.from_pretrained("dnagpt/gene_eng_zh_de_es")
+tokenizer.pad_token = tokenizer.eos_token
+
+
+#分词函数
+def tokenize_function(example):
+    return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length")
+
+#构建分词后的数据集
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+
+#训练数据构建
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+
+#指标函数定义
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return {'accuracy': (predictions==labels).sum() / len(labels)}
+
+
+
+training_args = TrainingArguments(
+    output_dir="ds_job_dna_2222",
+    learning_rate=1e-5,
+    lr_scheduler_type="constant_with_warmup",
+    warmup_ratio=0.1,
+    optim='adamw_torch',
+    weight_decay=0.0,
+    seed=seed,  # 使用动态生成的随机种子
+    per_device_train_batch_size=64,
+    per_device_eval_batch_size=64,
+    num_train_epochs=4, #训练多少轮
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_strategy="epoch",
+    load_best_model_at_end=True
+)
+
+#模型定义，文本分类模型
+model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gene_eng_zh_de_es", num_labels=2)
+model.config.pad_token_id = model.config.eos_token_id
+
+trainer = Trainer(
+    model,
+    training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train() #模型训练
+
+
+
+
+#模型测试 蛋白质数据集
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+    
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+result["dna_protein_pair_full"] = ret
+
+
+
+
+#############################################################
+#模型测试 蛋白质数据集，随机版本
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.5) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+
+result["dna_protein_pair_rand_full"] = ret
+
+
+
+print(json.dumps(result))
+

finetune_pt_100/gpt2_gene_zh_ft_en.jsonl

@@ -0,0 +1,100 @@
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+{"seed": 1, "dna_protein_pair_full": {"accuracy": 0.5775, "f1": 0.5212464589235127}, "dna_protein_pair_rand_full": {"accuracy": 0.52425, "f1": 0.4945551128818061}}
+{"seed": 2, "dna_protein_pair_full": {"accuracy": 0.5975, "f1": 0.5413105413105413}, "dna_protein_pair_rand_full": {"accuracy": 0.505625, "f1": 0.4625628482130724}}
+{"seed": 3, "dna_protein_pair_full": {"accuracy": 0.4575, "f1": 0.5353319057815846}, "dna_protein_pair_rand_full": {"accuracy": 0.52675, "f1": 0.5437454808387563}}
+{"seed": 4, "dna_protein_pair_full": {"accuracy": 0.4125, "f1": 0.35967302452316074}, "dna_protein_pair_rand_full": {"accuracy": 0.55825, "f1": 0.44936117170458084}}
+{"seed": 5, "dna_protein_pair_full": {"accuracy": 0.45, "f1": 0.6140350877192983}, "dna_protein_pair_rand_full": {"accuracy": 0.499, "f1": 0.6573187414500684}}
+{"seed": 6, "dna_protein_pair_full": {"accuracy": 0.4475, "f1": 0.21908127208480566}, "dna_protein_pair_rand_full": {"accuracy": 0.503, "f1": 0.23977055449330784}}
+{"seed": 7, "dna_protein_pair_full": {"accuracy": 0.6325, "f1": 0.5333333333333333}, "dna_protein_pair_rand_full": {"accuracy": 0.519875, "f1": 0.40495739736638264}}
+{"seed": 8, "dna_protein_pair_full": {"accuracy": 0.4825, "f1": 0.25806451612903225}, "dna_protein_pair_rand_full": {"accuracy": 0.517375, "f1": 0.2346878097125867}}
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+{"seed": 75, "dna_protein_pair_full": {"accuracy": 0.4725, "f1": 0.6011342155009451}, "dna_protein_pair_rand_full": {"accuracy": 0.4735, "f1": 0.5990862364363221}}
+{"seed": 76, "dna_protein_pair_full": {"accuracy": 0.5725, "f1": 0.5714285714285714}, "dna_protein_pair_rand_full": {"accuracy": 0.566375, "f1": 0.5439726567635073}}
+{"seed": 77, "dna_protein_pair_full": {"accuracy": 0.4625, "f1": 0.16342412451361868}, "dna_protein_pair_rand_full": {"accuracy": 0.53875, "f1": 0.3151447661469933}}
+{"seed": 78, "dna_protein_pair_full": {"accuracy": 0.505, "f1": 0.029411764705882353}, "dna_protein_pair_rand_full": {"accuracy": 0.504875, "f1": 0.02221673660824488}}
+{"seed": 79, "dna_protein_pair_full": {"accuracy": 0.41, "f1": 0.44075829383886256}, "dna_protein_pair_rand_full": {"accuracy": 0.54075, "f1": 0.5027070925825663}}
+{"seed": 80, "dna_protein_pair_full": {"accuracy": 0.5, "f1": 0.6655518394648829}, "dna_protein_pair_rand_full": {"accuracy": 0.5, "f1": 0.660671869697998}}
+{"seed": 81, "dna_protein_pair_full": {"accuracy": 0.5475, "f1": 0.35125448028673834}, "dna_protein_pair_rand_full": {"accuracy": 0.50875, "f1": 0.33749157113958195}}
+{"seed": 82, "dna_protein_pair_full": {"accuracy": 0.4075, "f1": 0.5683060109289617}, "dna_protein_pair_rand_full": {"accuracy": 0.534625, "f1": 0.6613915416098226}}
+{"seed": 83, "dna_protein_pair_full": {"accuracy": 0.395, "f1": 0.550185873605948}, "dna_protein_pair_rand_full": {"accuracy": 0.52825, "f1": 0.6432217810550198}}
+{"seed": 84, "dna_protein_pair_full": {"accuracy": 0.575, "f1": 0.4178082191780822}, "dna_protein_pair_rand_full": {"accuracy": 0.517625, "f1": 0.410929629064265}}
+{"seed": 85, "dna_protein_pair_full": {"accuracy": 0.4625, "f1": 0.6126126126126126}, "dna_protein_pair_rand_full": {"accuracy": 0.499625, "f1": 0.6507285577174766}}
+{"seed": 86, "dna_protein_pair_full": {"accuracy": 0.495, "f1": 0.10619469026548672}, "dna_protein_pair_rand_full": {"accuracy": 0.491625, "f1": 0.05965317919075144}}
+{"seed": 87, "dna_protein_pair_full": {"accuracy": 0.5125, "f1": 0.0}, "dna_protein_pair_rand_full": {"accuracy": 0.505625, "f1": 0.025622074402562207}}
+{"seed": 88, "dna_protein_pair_full": {"accuracy": 0.5675, "f1": 0.2171945701357466}, "dna_protein_pair_rand_full": {"accuracy": 0.482, "f1": 0.1518624641833811}}
+{"seed": 89, "dna_protein_pair_full": {"accuracy": 0.6175, "f1": 0.6450116009280742}, "dna_protein_pair_rand_full": {"accuracy": 0.51925, "f1": 0.5156171284634761}}
+{"seed": 90, "dna_protein_pair_full": {"accuracy": 0.5375, "f1": 0.11483253588516747}, "dna_protein_pair_rand_full": {"accuracy": 0.5065, "f1": 0.09491059147180192}}
+{"seed": 91, "dna_protein_pair_full": {"accuracy": 0.4775, "f1": 0.6193078324225865}, "dna_protein_pair_rand_full": {"accuracy": 0.49925, "f1": 0.6276951672862453}}
+{"seed": 92, "dna_protein_pair_full": {"accuracy": 0.5475, "f1": 0.11707317073170732}, "dna_protein_pair_rand_full": {"accuracy": 0.513625, "f1": 0.16555865322753593}}
+{"seed": 93, "dna_protein_pair_full": {"accuracy": 0.515, "f1": 0.039603960396039604}, "dna_protein_pair_rand_full": {"accuracy": 0.50925, "f1": 0.07623529411764705}}
+{"seed": 94, "dna_protein_pair_full": {"accuracy": 0.615, "f1": 0.3888888888888889}, "dna_protein_pair_rand_full": {"accuracy": 0.548625, "f1": 0.38262950931783213}}
+{"seed": 95, "dna_protein_pair_full": {"accuracy": 0.575, "f1": 0.38848920863309355}, "dna_protein_pair_rand_full": {"accuracy": 0.488375, "f1": 0.2013658536585366}}
+{"seed": 96, "dna_protein_pair_full": {"accuracy": 0.6325, "f1": 0.5558912386706949}, "dna_protein_pair_rand_full": {"accuracy": 0.4755, "f1": 0.38583138173302106}}
+{"seed": 97, "dna_protein_pair_full": {"accuracy": 0.5275, "f1": 0.25882352941176473}, "dna_protein_pair_rand_full": {"accuracy": 0.5235, "f1": 0.2427493047278506}}
+{"seed": 98, "dna_protein_pair_full": {"accuracy": 0.4575, "f1": 0.026905829596412557}, "dna_protein_pair_rand_full": {"accuracy": 0.509875, "f1": 0.08065650644783118}}
+{"seed": 99, "dna_protein_pair_full": {"accuracy": 0.61, "f1": 0.5214723926380368}, "dna_protein_pair_rand_full": {"accuracy": 0.51675, "f1": 0.4329715459078909}}

finetune_pt_100/gpt2_gene_zh_ft_test_others.py

@@ -0,0 +1,237 @@
+# import os
+
+# # 设置环境变量
+# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+# # 打印环境变量以确认设置成功
+# print(os.environ.get('HF_ENDPOINT'))
+
+# import subprocess
+# import os
+
+# result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True)
+# output = result.stdout
+# for line in output.splitlines():
+#     if '=' in line:
+#         var, value = line.split('=', 1)
+#         os.environ[var] = value
+        
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding
+from transformers import Trainer
+import evaluate
+import numpy as np
+from transformers import TrainingArguments
+from transformers import AutoModelForSequenceClassification
+import json
+from transformers import set_seed
+import random
+import numpy as np
+import torch
+from tqdm import tqdm
+import sys
+
+
+# seed = 42
+# random.seed(seed)
+# np.random.seed(seed)
+# torch.manual_seed(seed)
+# torch.cuda.manual_seed_all(seed)
+
+
+# 动态生成随机种子
+
+seed = int(sys.argv[1])
+lang = sys.argv[2] #语言
+
+#seed = random.randint(0, 10000)
+#print(f"Generated seed: {seed}")
+set_seed(seed)
+result = {}
+result["seed"] = seed
+
+
+
+# 假设你的JSON文件名为 'data.json' 并且每行是一个独立的JSON对象
+raw_datasets = load_dataset('paws-x', lang)  # 或者指定特定语言如 'zh' 表示中文,https://huggingface.co/datasets/google-research-datasets/paws-x
+
+#分词器
+tokenizer = AutoTokenizer.from_pretrained("dnagpt/gene_eng_zh")
+tokenizer.pad_token = tokenizer.eos_token
+
+
+#分词函数
+def tokenize_function(example):
+    return tokenizer(example["sentence1"], example["sentence2"], truncation=True,max_length=256, padding="max_length")
+
+#构建分词后的数据集
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+
+#训练数据构建
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+
+#指标函数定义
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return {'accuracy': (predictions==labels).sum() / len(labels)}
+
+
+
+training_args = TrainingArguments(
+    output_dir="ds_job_dna_2222",
+    learning_rate=1e-5,
+    lr_scheduler_type="constant_with_warmup",
+    warmup_ratio=0.1,
+    optim='adamw_torch',
+    weight_decay=0.0,
+    seed=seed,  # 使用动态生成的随机种子
+    per_device_train_batch_size=64,
+    per_device_eval_batch_size=64,
+    num_train_epochs=4, #训练多少轮
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_strategy="epoch",
+    load_best_model_at_end=True
+)
+
+#模型定义，文本分类模型
+model = AutoModelForSequenceClassification.from_pretrained("dnagpt/gene_eng_zh", num_labels=2)
+model.config.pad_token_id = model.config.eos_token_id
+
+trainer = Trainer(
+    model,
+    training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train() #模型训练
+
+
+
+
+#模型测试 蛋白质数据集
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair')['train'].train_test_split(test_size=0.1) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+    
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+result["dna_protein_pair_full"] = ret
+
+
+
+
+#############################################################
+#模型测试 蛋白质数据集，随机版本
+raw_datasets_dna_protein = load_dataset('dnagpt/gene_lan_transfer', 'dna_protein_pair_rand')['train'].train_test_split(test_size=0.5) #默认已经shuffle
+
+# 定义翻转标签的函数
+def flip_labels(example):
+    # 截取 sentence1 和 sentence2 的前 50 个字符,如果dna序列过长，bert分词会产生错误，只生成unk一个token
+    example["sentence1"] = example["sentence1"]
+    example["sentence2"] = example["sentence2"]
+    example['label'] = 1 - example['label']
+    return example
+
+# 应用翻转标签函数
+flipped_datasets_dna_protein = raw_datasets_dna_protein.map(flip_labels, batched=False)
+tokenized_datasets_dna_protein = flipped_datasets_dna_protein.map(tokenize_function, batched=True,num_proc=4)
+
+
+# 确保模型在 GPU 上
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()  # 进入推理模式，加速推理
+
+# 取出测试集数据
+test_dataset = tokenized_datasets_dna_protein["test"]
+
+# 预存预测结果
+preds = []
+labels = []
+
+# 批量大小（建议 64、128、256 视显存大小调整）
+batch_size = 64
+
+# 直接遍历数据集进行推理
+for i in tqdm(range(0, len(test_dataset), batch_size), desc="Predicting"):
+    batch = test_dataset[i : i + batch_size]
+    
+    # 转换为 Tensor 并移动到 GPU
+    inputs = {
+        "input_ids": torch.tensor(batch["input_ids"]).to(device),
+        "attention_mask": torch.tensor(batch["attention_mask"]).to(device),
+    }
+    batch_labels = batch["label"]  # 原始标签
+
+    with torch.no_grad():  # 关闭梯度计算，减少内存占用
+        outputs = model(**inputs)
+        batch_preds = torch.argmax(outputs.logits, axis=-1).cpu().numpy()  # 取最大概率的类别
+
+    preds.extend(batch_preds)
+    labels.extend(batch_labels)
+metric = evaluate.load("glue", "mrpc")
+ret = metric.compute(predictions=preds, references=labels)
+
+
+
+result["dna_protein_pair_rand_full"] = ret
+
+
+
+print(json.dumps(result))
+

finetune_pt_100/run_ft_all.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+export HF_ENDPOINT=https://hf-mirror.com
+lang=$1
+for ((i=0;i<100;i++))
+do
+	echo "----------------------------------------------"$i
+	python gpt2_gene_zh_ft_test_others.py $i $lang >> gpt2_gene_zh_ft_${lang}.json
+done