chinese_alpaca_2_13b_eval_100k.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j-0Paxc3wNYn",
    "outputId": "d5952c77-fba2-4d57-ebab-0920c1a93c55"
   },
   "outputs": [],
   "source": [
    "# !pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 trl==0.4.7 transformers[sentencepiece]==4.31.0 evaluate bert_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "Cn8DZxEhwSFb"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    GenerationConfig,\n",
    "    HfArgumentParser,\n",
    "    LlamaTokenizer,\n",
    "    TrainingArguments,\n",
    "    pipeline,\n",
    "    logging,\n",
    ")\n",
    "from peft import LoraConfig, PeftModel\n",
    "from trl import SFTTrainer\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PCi8Ol7KwTuX",
    "outputId": "34a4dfb3-cf29-4a28-d9a8-fbc55969c368"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(0)\n",
    "    device_map = {\"\": 0}\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "    device_map = \"cpu\"\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "G5B2A-FywURR"
   },
   "outputs": [],
   "source": [
    "# The model that you want to train from the Hugging Face hub\n",
    "model_name = \"chenqile09/chinese-alpaca-2-LoRA-13B-couplet-100k\"\n",
    "\n",
    "# The instruction dataset to use\n",
    "dataset_name = \"chenqile09/llama2-chinese-couplet-100k\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "woOfBxXNwyaz"
   },
   "outputs": [],
   "source": [
    "# Activate 4-bit precision base model loading\n",
    "use_4bit = True\n",
    "\n",
    "# Compute dtype for 4-bit base models\n",
    "bnb_4bit_compute_dtype = \"float16\"\n",
    "\n",
    "# Quantization type (fp4 or nf4)\n",
    "bnb_4bit_quant_type = \"nf4\"\n",
    "\n",
    "# Activate nested quantization for 4-bit base models (double quantization)\n",
    "use_nested_quant = False"
   ]
  },
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    {
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       "model_id": "5afb54c1681f459da5f65d27672a4b9a",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7091c89ba3dc42c88db55fc2edf0f657",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "model_id": "2fd12a53d81f489db1101530c7355581",
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "888bb74d4cc94ad88ad8fa54d6fc72c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51a1012da94946b5baeebf5dff41ff89",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/100000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31b9928786814b93a66840f62b8389dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/4000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load dataset\n",
    "dataset = load_dataset(dataset_name, split=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vkZ6HEShw5jm",
    "outputId": "488d77c8-2acc-4c1d-e69f-45618723ac49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.float16\n"
     ]
    }
   ],
   "source": [
    "# Load tokenizer and model with QLoRA configuration\n",
    "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
    "\n",
    "print(compute_dtype)\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=use_4bit,\n",
    "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
    "    bnb_4bit_compute_dtype=compute_dtype,\n",
    "    bnb_4bit_use_double_quant=use_nested_quant,\n",
    ")"
   ]
  },
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    "id": "tZ8EsgoVw9Iu",
    "outputId": "1081c5ee-d4b2-40d4-dd9b-f848a9d69ded"
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   "outputs": [
    {
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       "model_id": "7099352d2ce04df79cbd5d9a36f7d19b",
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    },
    {
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    {
     "data": {
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       "model_id": "ff23607baa0d4d5db5af843051a4621e",
       "version_major": 2,
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=device_map\n",
    ")\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151,
     "referenced_widgets": [
      "e680ab7dd4d648d9b6b1138323ea84de",
      "df0d0c1504604e09a00628c310f8f585",
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      "8224919613c64bcda6592335f5ee8b6b",
      "b9d0a137364947289b38bd842ae60974",
      "2273017ded2a4d37965047c2bdc34241",
      "3f2a7015c2df45f5876e574eb31efdc5",
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      "83e7c8e96f9d4465bceec297c99b55a4",
      "c2df6db9185b4861bfba97c69f38f349",
      "f1da66766a22422f95fb18307ff39e87",
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      "af9a3d70dabc4eb2b2f0e1d2bc195f54",
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      "c8e1a0fec27d4cf3b2e3302319ff9279",
      "fce2a1b90c174bf7b060f1025f4d72c6"
     ]
    },
    "id": "dvharEXmxlb-",
    "outputId": "00dd4158-d628-4592-bf1e-d35427be824b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03d8caa51640449eb0f0f5611a22e9b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.model:   0%|          | 0.00/844k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a980a0cda23841bc8f15481723f952e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c1f66a784904e29bdb744326f738005",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/766 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using the legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at https://github.com/huggingface/transformers/pull/24565\n"
     ]
    }
   ],
   "source": [
    "# Load tokenizer\n",
    "tokenizer = LlamaTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TXxUzRubTBDu"
   },
   "source": [
    "## Set up eval configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Px3_oZ2zxqXd"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import evaluate\n",
    "import numpy as np\n",
    "\n",
    "# Ignore warnings\n",
    "logging.set_verbosity(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "wauCI-OfxtPE"
   },
   "outputs": [],
   "source": [
    "# prompt generation and helper function to generate output\n",
    "COUPLET_SYSTEM_PROMPT = \"\"\"你是一个写对联的助手，你会根据给出的上联来写下联。上联和下联的字数要一致，并且平仄要对应。\"\"\"\n",
    "\n",
    "TEMPLATE = (\n",
    "    \"[INST] <<SYS>> {system_prompt} <</SYS>> {instruction} [/INST]\"\n",
    ")\n",
    "\n",
    "def generate_prompt(instructions, system_prompt=COUPLET_SYSTEM_PROMPT):\n",
    "    return [TEMPLATE.format_map({'instruction': instruction,'system_prompt': system_prompt}) for instruction in instructions]\n",
    "\n",
    "def generate_output_from_llm(model, tokenizer, generation_config, instructions, system_prompt=COUPLET_SYSTEM_PROMPT):\n",
    "\n",
    "    input_texts = generate_prompt(instructions, system_prompt)\n",
    "    inputs = tokenizer(input_texts, return_tensors=\"pt\", padding=True)\n",
    "    generation_outputs = model.generate(\n",
    "        input_ids = inputs[\"input_ids\"].to(device),\n",
    "        attention_mask = inputs['attention_mask'].to(device),\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        pad_token_id=tokenizer.pad_token_id,\n",
    "        generation_config = generation_config\n",
    "    )\n",
    "\n",
    "    outputs = [tokenizer.decode(s, skip_special_tokens=True) for s in generation_outputs]\n",
    "    responses = [output.split(\"[/INST]\")[-1].strip() for output in outputs]\n",
    "    return responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "kI71nD8pxrq6"
   },
   "outputs": [],
   "source": [
    "# Set up generation config\n",
    "generation_config = GenerationConfig(\n",
    "    temperature=0.2,\n",
    "    top_k=40,\n",
    "    top_p=0.9,\n",
    "    do_sample=True,\n",
    "    num_beams=1,\n",
    "    repetition_penalty=1.1,\n",
    "    max_new_tokens=400\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xEEsmDJZTFOD"
   },
   "source": [
    "## Run text generation on a few inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "nygaWf2Lxu-2"
   },
   "outputs": [],
   "source": [
    "# some example inputs\n",
    "# https://github.com/minaxixi/ai-couplet\n",
    "instructions = [\n",
    "    \"欲把心事付瑶琴\",\n",
    "    \"半衾幽梦香初散\",\n",
    "    \"一曲清音韵未央\",\n",
    "    \"应是飞鸿踏泥雪\",\n",
    "    \"云破月来花弄影\",\n",
    "    \"别后相思空一水\",\n",
    "    \"书中自有黄金屋\",\n",
    "    \"花谢花飞花满天\",\n",
    "    \"杨柳岸晓风残月\",\n",
    "    \"上海自来水来自海上\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EUYS9_DQeVvQ",
    "outputId": "86f40b85-ba96-4668-cdfa-4a7c608d4f9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "欲把心事付瑶琴;常将相思寄月轮\n",
      "半衾幽梦香初散;一枕相思泪未干\n",
      "一曲清音韵未央;半生苦水泪常流\n",
      "应是飞鸿踏泥雪;莫非白鹤过青天\n",
      "云破月来花弄影;风清水净鸟鸣春\n",
      "别后相思空一水;相逢泪眼满双眸\n",
      "书中自有黄金屋;笔底常留锦绣图\n",
      "花谢花飞花满天;月圆月缺月无边\n",
      "杨柳岸晓风残月;桃花源春雨秋风\n",
      "上海自来水来自海上;广州有线电视有线广州\n"
     ]
    }
   ],
   "source": [
    "# Generate responses in batch, and then print out\n",
    "\n",
    "responses = generate_output_from_llm(\n",
    "    model,\n",
    "    tokenizer,\n",
    "    generation_config,\n",
    "    instructions,\n",
    "    COUPLET_SYSTEM_PROMPT\n",
    ")\n",
    "\n",
    "for instruction, response in zip(instructions, responses):\n",
    "    print(instruction+';'+response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q8xlWlKyTU6O"
   },
   "source": [
    "## Run eval on the validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "DrTG_GOmj37o"
   },
   "outputs": [],
   "source": [
    "# batch the dataset into the dataloader\n",
    "ds_loader = torch.utils.data.DataLoader(\n",
    "    dataset,\n",
    "    batch_size=16, # tunable based on the GPU VRAM. 64 is good for 16GB VRAM (V100)\n",
    "    num_workers=4,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NKLD_kegkBfX",
    "outputId": "2e0accba-fb82-4030-84ff-9f6ada07403d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LlamaForCausalLM(\n",
       "  (model): LlamaModel(\n",
       "    (embed_tokens): Embedding(55296, 5120, padding_idx=0)\n",
       "    (layers): ModuleList(\n",
       "      (0-39): 40 x LlamaDecoderLayer(\n",
       "        (self_attn): LlamaAttention(\n",
       "          (q_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
       "          (k_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
       "          (v_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
       "          (o_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
       "          (rotary_emb): LlamaRotaryEmbedding()\n",
       "        )\n",
       "        (mlp): LlamaMLP(\n",
       "          (gate_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
       "          (up_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
       "          (down_proj): Linear4bit(in_features=13824, out_features=5120, bias=False)\n",
       "          (act_fn): SiLUActivation()\n",
       "        )\n",
       "        (input_layernorm): LlamaRMSNorm()\n",
       "        (post_attention_layernorm): LlamaRMSNorm()\n",
       "      )\n",
       "    )\n",
       "    (norm): LlamaRMSNorm()\n",
       "  )\n",
       "  (lm_head): Linear(in_features=5120, out_features=55296, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eval mode\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195,
     "referenced_widgets": [
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    },
    "id": "lBgf84PDkfsR",
    "outputId": "32afbc0f-ba30-4100-bac9-1061f64a15a9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 250/250 [10:29<00:00,  2.52s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f88cc15465d4a98ae779ec59a58fd23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cc9aa82cec347708f9a877fe17740b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/624 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0193f485b5a8408f93be7a72fa97ef3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/110k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da5379f8c1b44cce81ff1371de413458",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/412M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run eval on bert-score and also save the results\n",
    "bertscore = evaluate.load(\"bertscore\")\n",
    "\n",
    "inputs = []\n",
    "preds = []\n",
    "references = []\n",
    "\n",
    "for i, batch in tqdm(list(enumerate(ds_loader))):\n",
    "    with torch.no_grad():\n",
    "        predictions = generate_output_from_llm(\n",
    "            model,\n",
    "            tokenizer,\n",
    "            generation_config,\n",
    "            batch['input'],\n",
    "            COUPLET_SYSTEM_PROMPT\n",
    "        )\n",
    "\n",
    "    inputs.extend(batch['input'])\n",
    "    preds.extend(predictions)\n",
    "    references.extend(batch['output'])\n",
    "    bertscore.add_batch(references=batch['output'], predictions=predictions)\n",
    "\n",
    "result = bertscore.compute(lang='zh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yYirNkQLluat",
    "outputId": "a01f77f9-3eec-48cc-8ba3-0728930feedd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 0.6630038371756672, 'recall': 0.6624025095552206, 'f1': 0.6626842786818743}\n"
     ]
    }
   ],
   "source": [
    "# print the result\n",
    "results = dict()\n",
    "\n",
    "for key, val in result.items():\n",
    "    if key in {'precision', 'recall', 'f1'}:\n",
    "        results[key] = np.mean(val)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a dataset using the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import Dataset, DatasetDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "pQVIuMTiPhXj"
   },
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame(\n",
    "    {'input': inputs,\n",
    "     'prediction': preds,\n",
    "     'reference': references\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input</th>\n",
       "      <th>prediction</th>\n",
       "      <th>reference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>腾飞上铁，锐意改革谋发展，勇当千里马</td>\n",
       "      <td>崛起中西，雄心创业创辉煌，喜作百强羊</td>\n",
       "      <td>和谐南供，安全送电保畅通，争做领头羊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>风弦未拨心先乱</td>\n",
       "      <td>月影初生梦不休</td>\n",
       "      <td>夜幕已沉梦更闲</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>花梦粘于春袖口</td>\n",
       "      <td>柳丝缠绕燕衣襟</td>\n",
       "      <td>莺声溅落柳枝头</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>晋世文章昌二陆</td>\n",
       "      <td>唐时翰墨盛三家</td>\n",
       "      <td>魏家词赋重三曹</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>一句相思吟岁月</td>\n",
       "      <td>半生寂寞寄春秋</td>\n",
       "      <td>千杯美酒醉风情</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                input          prediction           reference\n",
       "0  腾飞上铁，锐意改革谋发展，勇当千里马  崛起中西，雄心创业创辉煌，喜作百强羊  和谐南供，安全送电保畅通，争做领头羊\n",
       "1             风弦未拨心先乱             月影初生梦不休             夜幕已沉梦更闲\n",
       "2             花梦粘于春袖口             柳丝缠绕燕衣襟             莺声溅落柳枝头\n",
       "3             晋世文章昌二陆             唐时翰墨盛三家             魏家词赋重三曹\n",
       "4             一句相思吟岁月             半生寂寞寄春秋             千杯美酒醉风情"
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